Academic literature on the topic 'Condition Monitoring of DC Motor'

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Journal articles on the topic "Condition Monitoring of DC Motor"

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PRAJAKTA, RAJENDRA GOSAVI. "DC MOTOR CONDITION MONITORING USING ARTIFICIAL NEURAL NETWORK." IJIERT - International Journal of Innovations in Engineering Research and Technology 5, no. 4 (2018): 105–9. https://doi.org/10.5281/zenodo.1454005.

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<strong><strong>&nbsp;</strong>Electrical motors especially DC motor plays very important role in the industry in heavy machining industry,where starting torque plays very important role. The use of DC motor is done in a maximum way,to avail advantages of high starting toque,preferably in crane applications . The improper maintenance of industrial motors is very much necessary to avoid the production loss. In this paper artificial neural network control (ANNC) based technique is proposed f or analysis of vibration,power consumption etc. In last decade ANN based controllers even for non linear systems. The test results analyzed for steady as well as dynamic conditions. From the obtained results it is said that ANN controller is more than PI, PID controller.</strong> <strong>https://www.ijiert.org/paper-details?paper_id=141181</strong>
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Naqeeb, Ullah, Shakeel Akram Muhammad, Ullah Buzdar Hameed, Ur Rehman Atiq, Abbas Khan Muhammad, and Khan Saifullah. "ZigBee-based Parameter Monitoring and Controlling Scheme for Multiple DC Motors." Indian Journal of Science and Technology 13, no. 6 (2020): 725–34. https://doi.org/10.17485/ijst/2020/v13i06/149098.

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Abstract <strong>Objective/aim:</strong>&nbsp;This study suggests a safe and economical wireless control and monitoring system for multiple DC motors based on ZigBee wireless communication. <strong>Methods:</strong>&nbsp;A set of sensors is used to control and monitor the information regarding the multiple DC motors parameters such as voltage, speed, temperature, and over-current and then transmit this information through the wireless ZigBee Protocol. A microcontroller is used for information collection and storage. According to monitored information, the DC motor will be stopped or started by the control signal via the computer interface produced with ZigBee when a certain condition is accomplished. <strong>Findings:</strong>&nbsp;The developed system can perform various operations like the automatic on/off of multiple DC motors, forward &amp; reverse, speed control, checking overload condition, current measuring, and voltage measuring. The parameters: voltage, current, temperature, revolution per minute (RPM) can be detected in real-time by sensors and transferred to the host computer, displayed and store in an excel file. A ZigBee device is used to receive and transmit data efficiently from microcontroller to the PC and vice versa. In the past, similar systems were used for a single DC motor which was expensive such as PLC and Wi-Fi. In this work, a cost-effective ZigBee-based system is used for multiple DC motors. <strong>Applications/improvements:</strong>&nbsp;The proposed system can be further improved and modified for the real-time measuring system for AC machines. Moreover, the proposed wireless monitoring system can be enhanced with artificial intelligence. <strong>Keyword:</strong> DC Motor, Wireless Control and ZigBee Module, Microcontroller, Monitoring System.
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Szecsi, Tamas. "A DC motor based cutting tool condition monitoring system." Journal of Materials Processing Technology 92-93 (August 1999): 350–54. http://dx.doi.org/10.1016/s0924-0136(99)00182-x.

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Slamet Winardi, Didik Dwi Suharso, Hendra Purnomo, and Arief Budijanto. "Battery Power Control and Monitoring System with Internet of Things Technology." IJCONSIST JOURNALS 3, no. 1 (2021): 07–12. http://dx.doi.org/10.33005/ijconsist.v3i1.56.

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Along with the development of battery/ACCU technology, the ACCU charging and loading system has also developed manually or automatically. In this paper, the results of research on the manufacture of tools that function to monitor and control the charging and loading of electrical power will be explained from batteries that are burdened with lights and DC motors through a control panel or android smart phone with IoT technology. This tool is designed to monitor 2 ACCUs, namely ACCU1 and ACCU2. ACCU1 is loaded with lamp and parallel with DC motor load while ACCU2 is loaded with DC motor and parallel with lamp load. If ACCU1 and ACCU2 are full, ACCU1 is loaded with only one lamp and ACCU2 is only loaded with DC motor. For example, ACCU1's electrical power is still within the limits that can be loaded with lights and DC motors while ACCU2 can't be loaded because electric power is not capable of being loaded with DC motors, the DC motor's load will be transferred to ACCU1. So that ACCU1 is loaded with DC lamps and motors and ACCU2 is charging the electricity, as well as for the opposite condition. The design of this tool uses the main component of the ESP32 microcontroller, where this component has been integrated with the wifi module and other supporting circuit modules, namely the current sensor circuit, voltage sensor and OLED display. The software design consists of a program code designer for hardware using C++ and an android smart phone application design using MIT App Inventor. The results of this study are in accordance with the design specifications, namely the device can be used to control charging and loading as well as monitor ACCU1 and ACCU2 electrical power on OLED screens and on Android smart phones screens
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Круглова, Татьяна, and Tat'yana Kruglova. "STUDY OF THE TECHNICAL CONDITION OF ELECTRIC DRIVE UNDER DIFFERENT LOADING CONDITIONS." Bulletin of Belgorod State Technological University named after. V. G. Shukhov 4, no. 3 (2019): 106–16. http://dx.doi.org/10.34031/article_5ca1f6347299b0.43047357.

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The main elements of the process equipment are DC and AC motors that largely determine its reliability and efficiency of operation. The constant monitoring of technical condition by methods of technical diagnostics allows a significant extension of equipment life and reduces financial costs. To implement this approach, specialized methods are required. They allow to determine the technical condition of DC and AC motors with a high degree of reliability, distinguishing their faulty state from changing the operating mode. Diagnostics should be performed in the mode of equipment operation; therefore, the use of complex measuring devices is not permissible. This article presents the results of search studies of diagnosis method that meets the above-mentioned requirements. Current, voltage and vibration are selected as diagnostic parameters. It is proposed to analyze them by the wavelet transform. As a result of numerous experiments, the relationship between changes in the wavelet transform coefficients on characteristic scales has been established. This allows to determine the technical condition of the electric motor and the mode of its load, on the basis of which a diagnostic method has been developed using neural networks.
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Glowacz, Adam. "DC Motor Fault Analysis with the Use of Acoustic Signals, Coiflet Wavelet Transform, and K-Nearest Neighbor Classifier." Archives of Acoustics 40, no. 3 (2015): 321–27. http://dx.doi.org/10.1515/aoa-2015-0035.

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AbstractThis paper focuses on testing the monitoring system of the Direct Current motor. This system gives the possibility of diagnosing various types of failures by means of analysis of acoustic signals. The applied method is based on a study of acoustic signals generated by the DC motor. A study plan of the DC motor’s acoustic signal was proposed. Studies were conducted for a faultless DC motor and Direct Current motor with 3 shorted rotor coils. Coiflet wavelet transform and K-Nnearest neighbor classifier with Euclidean distance were used to identify the incipient fault. This approach keeps the motor operating in acceptable condition for a long time and is also inexpensive.
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Ghorai, Arindam. "Hybrid Powered IoT-Based BLDC Motor Monitoring System for Electric Vehicles." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 799–806. https://doi.org/10.22214/ijraset.2025.67385.

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This paper proposes a hybrid powered internet of things (IoT) system for monitoring the condition of brushless DC Motors (BLDC) in electric vehicles (EVs). This IoT system has been designed and developed using a hybrid energy storage system that helps to enhance the life span of motor and battery. This system employs a range of sensors, including load, vibration, temperature, voltage and current sensors integrated with an Arduino UNO and ESP8266 microcontroller. These components gather real time data from motors side and transmitted to the cloud server. The IoT system ensures that the system is capable of capturing and reporting vital motor parameters to the cloud server and an automatic notification is sent to operators when Motors abnormality is detected. An effective BLDC motor monitoring system can be achieved by early monitoring with instant notification to operators. The main benefits are cost reduction of maintenance, increased reliability, optimised motor performances and improved overall efficiency of the system.
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Babu, G. Sreedhar, A. Lingamurthy, and A. S. Sekhar. "Condition monitoring of brushless DC motor-based electromechanical linear actuators using motor current signature analysis." International Journal of Condition Monitoring 1, no. 1 (2011): 20–32. http://dx.doi.org/10.1784/204764211798089066.

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Lu, Yean. "DC Motor Control Technology Based on Multisensor Information Fusion." Computational Intelligence and Neuroscience 2022 (July 1, 2022): 1–10. http://dx.doi.org/10.1155/2022/1447333.

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To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one of the most widely used motors at present. It has excellent speed regulation performance and is easy to control. It is widely used in applications that require high motor startup and speed regulation characteristics. This research mainly discusses DC motor control technology. Evidence theory can combine various fault information at different levels to enhance mutual support between pieces of evidence, thereby improving the accuracy of motor fault detection. Based on the steps of signal processing, feature extraction, feature dimensionality reduction, and state recognition, the research on the state recognition method of belt conveyor drive motor based on multisource information fusion is carried out. By studying the multisource information fusion, this paper proposes a two-stage belt conveyor drive motor information fusion model based on the optimal D-S evidence theory. The correct identification rate of broken rotor bars during fault monitoring is 99.8%. This method divides the specific motor fault feature set into multiple fault subspaces and uses different diagnostic neural networks and different fault feature parameters for local diagnosis, respectively. The scheme designed in this study significantly improves the recognition accuracy of the motor in the same working condition and under variable working conditions. The drive motor state recognition and intelligent decision-making system designed by combining the results of multisource information fusion can effectively describe the fault type and has strong operability.
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Sinan Cabuk, Ali. "EXPERIMENTAL DATA ACQUISITION OF VIBRATION FORCE IN DIRECT CURRENT MOTOR FOR MINI SUMO ROBOT." Applied Researches in Technics, Technologies and Education 7, no. 1 (2019): 48–57. http://dx.doi.org/10.15547/artte.2019.01.006.

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Vibration is the oscillating or periodic movement of electric motors from its position to a certain point, which has effect on performance. The vibration of electrical motor is essential causes of machine failure. Therefore, reaction force of electric motors is the most important parameter for their working life. It is monitoring and minimize the vibration generated by the electric motor. One of the objectives of this study is to present that forced vibration analysis is compatible technique for diagnosing electromagnetic and mechanical problems in electric motors. In this study aims at vibration analysis on Direct Current (DC) motor, that is the most preferred motors for robotic application. DC motor used in this study has 15 W output power, 400 min-1 shaft speed and 12 V terminal voltage, which is widely implemented in robot competition. Vibration forced was measured with accelerometer sensor on the motor. Test vibration results of the motor were checked it whether limiting value or not. In this study, vibration effect of DC motor in mini sumo robot is presented with during competition and smooth road conditions.
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Dissertations / Theses on the topic "Condition Monitoring of DC Motor"

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Wen, Xin. "A hybrid intelligent technique for induction motor condition monitoring." Thesis, University of Portsmouth, 2011. https://researchportal.port.ac.uk/portal/en/theses/a-hybrid-intelligent-technique-for-induction-motor-condition-monitoring(bafbf73a-0813-44f8-8f00-9212cc54c890).html.

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The objective of this research is to advance the field of condition monitoring and fault diagnosis for induction motors. This involves processing the signals produced by induction motors, classifying the types and estimating the severity of induction motors faults. A typical process of condition monitoring and fault diagnosis for induction motors consists of four steps: data acquisition, signal analysis, fault detection and post-processing. A description of various kinds of faults that can occur in induction motors is presented. The features reflecting faults are usually embedded in transient motor signals. The signal analysis is a very important step in the motor fault diagnosis process, which is to extract features which are related to specific fault modes. The signal analysis methods available in feature extraction for motor signals are discussed. The wavelet packet decomposition results consist of the time-frequency representation of a signal in the same time, which is inherently suited to the transient events in the motor fault signals. The wavelet packet transform-based analysis method is proposed to extract the features of motor signals. Fault detection has to establish a relationship between the motor symptoms and the condition. Classifying motor condition and estimating the severity of faults from the motor signals have never been easy tasks and they are affected by many factors. AI techniques, such as expert system (ES), fuzzy logic system (FLS), artificial neural network (ANN) and support vector machine (SVM), have been applied in fault diagnosis of very complex system, where accurate mathematical models are difficult to be built. These techniques use association, reasoning and decision making processes as would the human brain in solving diagnostic problems. ANN is a computation and information processing method that mimics the process found in biological neurons. But when ANN-based methods are used for fault diagnosis, local minimums caused by the traditional training algorithms often result in large approximation error that may destroy their reliability. In this research, a novel method of condition monitoring and fault diagnosis for induction motor is proposed using hybrid intelligent techniques based on WPT. ANN is trained by improved genetic algorithm (IGA). WPT is used to decompose motor signals to extract the feature parameters. The extracted features with different frequency resolutions are used as the input of ANN for the fault diagnosis. Finally, the proposed method is tested in 1.5 kW and 3.7 kW induction motor rigs. The experimental results demonstrate that the proposed method improves the sensitivity and accuracy of the ANN-based methods of condition monitoring and fault diagnosis for induction motors.
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Kambisseri, Roby Neelu. "Wireless communication using metasurfaces for condition monitoring in motor." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246051.

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Wireless sensors are used widely for condition monitoring in electric machines. The metal enclosure of an electric motor restricts the signal from sensors to radiate outside. The signal from the metal cavity needs to be guided to the only opening in the enclosure, through a narrow gap between the stator and the rotating rotor. Gap waveguide technology is proposed as a solution by texturing the stator surface with electromagnetic band gap (EBG) structures. Arrays of periodic holey structures are used to realize the metasurface waveguide. Two Bravais lattice structures – square and hexagonal, are explored for guiding waves along a desired path in a parallel plate waveguide. Simulations are carried out to study the influence of various dimensions of the unit cells. A waveguide with hexagonal hole-type unitcell is designed and manufactured for experimental verification. The possibility of extending the same technology to cylindrical surface is confirmed by simulations.<br>Trådlösa sensorer används allmänt för tillståndsövervakning i elektriska maskiner. Metallhöljet hos en elektrisk motor begränsar signalen från sensorerna från att stråla utåt. Signalen från metallhåligheten behöver styras till den enda öppningen i höljet, genom ett smalt mellanrum mellan statorn och den roterande rotorn. Gap-vågledarteknik föreslås som en lösning genom att strukturera statorytan med elektromagnetiska bandgap-strukturer (EBG). Arrayer av periodiskt håliga strukturer används för att realisera metayt-vågledare. Två Bravais gitterkonstruktioner –kvadratiska och sexkantiga, undersöks för styrning av vågor längs en önskad väg i en parallellplattvågledare. Simuleringar utförs för att studera påverkan av olika dimensioner hos enhetscellerna. En vågledare med hexagonal håltypsenhetscell är konstruerad och tillverkad för experimentell verifiering. Möjligheten att utvidga samma teknik till cylindrisk yta bekräftas genom simuleringar.
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Moore, Sean. "Online condition monitoring of lithium ion batteries by performing impedance spectroscopy using a DC-DC converter." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29186.

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Li-Ion batteries are currently being used extensively in a variety of applications such as portable electronics, electric vehicles and grid storage applications, due to the high demand for high power and high energy density storage batteries. However, the usage of Li-Ion batteries requires extensive condition monitoring to increase overall performance and life expectancy. This research is focused on implementing rapid online condition monitoring techniques, using signal injection via the associated power converter in the battery management system. The technique implemented in this work is known as Electrochemical Impedance Spectroscopy (EIS). EIS is a well-known technique, that has been used to characterize an electrochemical cell’s behaviour and state by monitoring changes in the cell's impedance. This is accomplished by injecting currents at different frequencies into the battery and measuring the voltage response. This technique is conventionally implemented, using standard Frequency Response Analysers (FRA), while the battery is disconnected from the load (offline) due to the long procedural times involved. This has limited the use of EIS to laboratory testing. In recent years, there has been literature regarding incorporating EIS testing into the battery system, which is done in mainly 2 ways, by including a linear amplifier into the system to inject the current perturbations directly, or by using the existing circuitry in the BMS (typically the DC-DC converter) to inject the current perturbations using various control techniques. Although, these strategies have been applied in literature for online systems, they are still riddled with a lengthy EIS measurement time issue. This work seeks to significantly reduce the associated testing time with the use broadband signals to implement Impedance Spectroscopy for online systems via the associated BMS converter. Broandband Impedance Spectroscopy (BIS) is implemented by injecting a signal with multiple frequencies concurrently as this allows for quicker measurement. The main contribution of this work is the real-time implementation of a multi-sine broadband excitation via a bi-directional converter that can be used in a varying system. The results obtained were compared to results from an industry standard FRA and showed to produce Nyquist plots with a reasonable error.
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Blödt, Martin Faucher Jean Regnier Jérémi. "Condition monitoring of mechanical faults in variable speed induction motor drives." Toulouse : INP Toulouse, 2006. http://ethesis.inp-toulouse.fr/archive/00000313.

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Al-Arbi, Salem. "Condition monitoring of gear systems using vibration analysis." Thesis, University of Huddersfield, 2012. http://eprints.hud.ac.uk/id/eprint/17821/.

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It is often impractical to measure vibrations directly at /or close to their sources when condition monitoring gearbox systems. It is common to measure the vibration distant from the source due to limited access to the component which is to be monitored. In addition, operating the gearbox under different loads and speeds also produces vibration signals within different components. Vibration measured in this way may be significantly distorted by the effect of signal transmission paths and interference from other sources. Therefore, suppression of distortions is a key issue for remote measurement based condition monitoring. In this research work, the influences of transducer locations and operating conditions on the vibration signal have been investigated on a typical gearbox transmission system for the detection of faults induced within the gearbox. Vibration signals corresponding to a healthy (baseline) and faulty conditions on two-stage helical gearbox at various load and speed levels were recorded. The baseline vibration data were examined using conventional methods in the time, frequency and the joint time-frequency domains, and are referenced for comparison with more advanced methods. Several parameters have been proposed for monitoring gear condition locally (gearbox casing) including time, frequency, and joint time-frequency domain representation. The results show that traditional signal processing techniques were insufficient for revealing fault detection information due to the low signal to noise ratio (SNR). This research also presents a mathematical model for the simulation of vibration signals in order to further understand the source of the vibration. The model represents a two stage gear system using a suitable stiffness function to represent the forces acting between each pair of gears. Rotational stiffness and damping are also used to simulate the angular motion of the gears and shafts. Results show that the frequency spectrum of acceleration outputs from the model take the expected form with peaks at the meshing frequency and associated harmonics. Furthermore, if the stiffness function between the first pair of gears is simulated with a broken tooth, and various degrees of damage, outputs from the simulation have similar sideband effects to the signals produced in the experimental investigation. In addition, the model also demonstrates that variation of load and speed produces a corresponding effect to that seen in the experiments. Consequently, although relatively simple, the mathematical model can be used to explain vibration mechanisms in real gearbox systems used in condition monitoring. Time synchronous averaging (TSA) has been applied to the vibration signals from the gearbox to remove random noise combined with the raw signal. The angular domain signal, the order spectrum and the order-frequency presentation were used to characterise gearbox vibration in these new domains in more detail. Results obtained following TSA were compared with those obtained through conventional analysis from waveform characteristics, spectrum patterns and corresponding feature parameters under different operating loads and fault conditions. In addition, continuous wavelet transform (CWT) of TSA was also compared with the conventional CWT results of raw signals to further characterise vibrations. As part of this research study, the vibration transmission path has been estimated using the frequency response function (FRF) technique. A response based estimation method has been developed to revise the base path and adapted to operating conditions for more accurate fault estimation. Both theoretical analysis and test results showed that improved diagnosis when the path information was included in vibration signal processing and feature selection. Finally, the vibration data recorded from the two accelerometers located on the gearbox casing and motor flange were analyzed using different signal processing methods to investigate the effect of path transmission (transducer location) on the detection and diagnosis of the seeded gear tooth faults. Results from the angular domain, the order spectrum and the order-frequency analysis are presented to demonstrate use of these techniques for fault detection in gearboxes and that the effect of path transmissions can be observed on the vibration signals. Results showed that CWT of the TSA signal could be used to detect and indicate the severity of the gear damage effectively even if vibration signals originated from a remote motor flange.
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Zheng, Lin. "Model-based condition monitoring of anti-lock braking systems." Thesis, University of Huddersfield, 2014. http://eprints.hud.ac.uk/id/eprint/20331/.

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The Anti-lock Braking System (ABS) is one of the most important safety features in modern vehicles. It is a device integrating complicated electronic systems, hydraulic systems and mechanical components. It is possible to produce faults in these systems due to extreme vehicle operating conditions, which may lead to the failure of the ABS. However, there has not been an effective mechanism available in current operation and service facilities, which allows the performance of the ABS to be checked on-board or at a service base. This research therefore aims to investigate and develop approaches which allow the ABS systems to be monitored in different ways. As the ABS is a highly integrated system, conventional monitoring methods cannot be applied to it directly. The primary objective of this research is to develop a condition monitoring model for a typical ABS system under different conditions and then to monitor the dynamic characteristics and performance of the ABS according to simulation and experimental results. The Rapid Control Prototype (RCP) technique is used by applying dSpace MicroAutoBoxII on the ABS controller. A full mathematical model has been developed to simulate the ABS system under different conditions and seeded fault conditions. This results in a full understanding of the characteristics of measurable variables such as wheel velocity and vehicle velocity. This work has led to the conclusion that a model-based condition monitoring approach is the method with the most potential for the monitoring of the ABS systems. To overcome inevitable measurement noise and model uncertainties, a Kalman filter (KF) has been designed and evaluated through both simulation data and experimental results. This has been found to have acceptable performance and has subsequently been incorporated into the model-based condition monitoring system. The performance of the model-based condition monitoring system has been evaluated using an ABS test system. The ABS test rig consists of the basic ABS components and also the dSpace MicroAutoBoxII components, together with NI data acquisition equipment. The ABS test rig developed in this research is highly flexible to allow experimental investigations under different fault conditions with different severities. It has demonstrated that the monitoring system can reliably detect different possible faults in the ABS such as speed sensor failure, solenoid valve sticking or stuck, hydraulic fluid leakage and pump efficiency loss. All these faults occur with high possibility according to a systematic failure mode analysis based on that of similar components. Obviously, there is still considerable work which needs to be carried out to adopt this system in industry. For example, interfaces to integrate this new system into existing vehicle electronics should be investigated. In addition, specific fault conditions from different vehicle manufacturers should be simulated to tailor the system to specific vehicles specifically.
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Sysoeva, Viktoriia. "Hidden Markov Model-Supported Machine Learning for Condition Monitoring of DC-Link Capacitors." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1595978044573618.

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Rylands, Naasef. "Condition monitoring of induction motors in the nuclear power station environment." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29686.

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The induction motor is a highly utilised electrical machine in industry, with the nuclear industry being no exception. A typical nuclear power station usually contains more than 1000 motors, where they are used in safety and non-safety application. The efficient and fault-free operation of this machine is critical to the safe and economical operation of any plant, including nuclear power stations. A comprehensive literature review was conducted that covered the functioning of the induction machine, its common faults and methods of detecting these faults. The Condition Based Maintenance framework was introduced in which condition monitoring of induction machines is an essential component. The main condition monitoring methods were explained with the main focus being on Motor Current Signature Analysis (MCSA) and the various methods associated with it. Three analysis methods were selected for further study, namely, Current Signature Analysis, Instantaneous Power Signature Analysis (IPSA) and Motor Square Current Signature Analysis (MSCSA). Essentially, the methodology used in this dissertation was to study the three common motor faults (bearings, stator and rotor cage) in isolation and compare the results to that of the healthy motor of the same type. The test loads as well as fault severity were varied where possible to investigate its effect on the fault detection scheme. The data was processed using an FFT based algorithm programed in MATLAB. The results of the study of the three spectral analysis techniques showed that no single technique is able to detect motor faults under all tested circumstances. The MCSA technique proved the most capable of the three techniques as it was able to detect faults under most conditions, but generally suffered poor results in inverter driven motor applications. The IPSA and MSCSA techniques performed selectively when compared to MCSA and were relatively successful when detecting the mechanical faults. The fact that the former techniques produce results at unique points in the spectrum would suggest that they are more suitable for verifying results. As part of a comprehensive condition monitoring scheme, as required by a large population of the motors on a nuclear power station, the three techniques presented in this study could readily be incorporated into the Condition Based Maintenance framework where the strengths of each could be exploited.
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Baqqar, Mabrouka. "Machine performance and condition monitoring using motor operating parameters through artificial intelligence techniques." Thesis, University of Huddersfield, 2015. http://eprints.hud.ac.uk/id/eprint/28315/.

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Condition monitoring (CM) of gearboxes is a necessary activity due to the crucial importance of gearboxes in power transmission in most industrial applications. There has long been pressure to improve measuring techniques and develop analytical tools for early fault detection in gearboxes. This thesis develops new gearbox monitoring methods by demonstrating that operating parameters (static data) obtained from machine control processes can be used, rather than parameters obtained from vibration and acoustic measurements. Such a development has important implications for the future of CM techniques because it could greatly simplify the measurement process. To monitor the gearbox under different operating and fault conditions based on the static data, three artificial intelligence (AI) techniques: a general regression neural network (GRNN), a back propagation neural network (BPNN), and an adaptive neuro-fuzzy inference system (ANFIS) have been used successfully to capture nonlinear variations of the electric motor current and control parameters such as load settings and temperatures. The three AI systems are taught the expected values of current; load and temperature for the gearbox in a given condition, and then measured values obtained from the gearbox with a known fault introduced are assessed by each of the AI models to indicate the presence of this abnormal condition. The experimental results show that each of GRNN, BPNN and ANFIS are adequate and are able to serve as an effective tool for gearbox condition monitoring and fault detection. The main contributions of this study is to examine the performance of a model based condition monitoring approach by using just operating parameters for fault detection in a two stage gearbox. A model for current prediction is developed using an ANFIS, GRNN and BPNN which captures the complicated inter-relations between measured variables, and uses direct comparison between the measured and predicted values for fault detection.
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Dlamini, Mpendulo. "Development of an induction motor condition monitoring test rig And fault detection strategies." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/8686.

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Includes bibliographical references.<br>This thesis sets out to develop an induction motor condition monitoring test rig to experimentally simulate the common faults associated with induction motors and to develop strategies for detecting these faults that employ signal processing techniques. Literature on basic concepts of induction motors and inverter drives, the phenomena of common faults associated with induction motors, the condition monitoring systems were intensively reviewed.
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Books on the topic "Condition Monitoring of DC Motor"

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Brealey, David, and Nicholas Hirsch. Diagnosis, assessment, and management of Guillain–Barré syndrome. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0246.

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The Guillain–Barré Syndrome describes a spectrum of acute inflammatory polyneuropathies and is the commonest cause of acute flaccid paralysis within the western world. The pathophysiology is complex and poorly understood, but appears to be an immune-mediated destruction of either the myelin sheath and/or the axons, predominantly of motor nerves. The clinical presentation is classically a rapid, ascending, flaccid paralysis, with minimal sensory deficit. This may ascend to involve respiratory or bulbar muscle function. These patients need careful monitoring and, if deteriorating, should be electively intubated and ventilated. Autonomic instability and sensory disturbance, including pain, is common. Treatment of the underlying condition relies upon immunomodulation with either intravenous immunoglobulin or plasma exchange. Supportive care is aimed at maintaining a safe airway, ventilatory support, and managing the complications of autonomic dysfunction and prolonged immobility. Mortality rates range up to 20%, but are significantly better in specialist neuromedical units. Survivors are often left with significant disability.
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Martins, Marielza R. Ismael. Transtornos de Aprendizagem: A abordagem multidisciplinar. Brazil Publishing, 2021. http://dx.doi.org/10.31012/978-65-5861-557-6.

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The themes included in this book, involving active collaborators, confirm the need to fill the gap for health and education professionals. By presenting information from reliable sources and step-by-step activities to implement interventions with visual and phonological, dysgraphic, dyscalculic and ADHD dyslexics, it aims to provide effective procedures for screening, evaluation, intervention selection and monitoring. Issues examined such as visual dyslexia analyze developmental dyslexia as a condition that has been associated with motor difficulties, but little is known about what is shared or differentiated between its subtypes. The assessment of dyscalculia, which is often neglected, is clearly presented providing a tool for assessment and addressing family or support networks of students with Learning Disorders deepens our understanding Understanding the development of the school allows us to recognize specific situations that are often ignored. All activities have multisensory instruction, that is, students use more than one sense at a time, and multisensory instruction offers students more than one way to make connections and learn concepts The contributors to this book are active researchers in the teachinglearning process and the objective was to expand information on Learning Disorders with content chosen in a selective manner, giving relevance to the multidisciplinary team.
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Book chapters on the topic "Condition Monitoring of DC Motor"

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Włódarczak, Krzysztof, Łukasz Grzymkowski, and Tomasz P. Stefański. "Condition-Based Monitoring of DC Motors Performed with Autoencoders." In Intelligent and Safe Computer Systems in Control and Diagnostics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16159-9_15.

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Lorencki, Jakub, and Stanisław Radkowski. "Switched Reluctance Motor Modelling Methods." In Applied Condition Monitoring. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31719-4_11.

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Spagnol, Marco, Luigi Bregant, and Alessandro Boscarol. "Electrical Induction Motor Higher Harmonics Analysis Based on Instantaneous Angular Speed Measurement." In Applied Condition Monitoring. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_3.

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Boudhraa, Safa, Alfonso Fernandez del Rincon, Mohamed Amine Ben Souf, Fakher Chaari, Mohamed Haddar, and Fernando Viadero. "Investigating the Electro-mechanical Interaction Between Helicoidal Gears and an Asynchronous Geared Motor." In Applied Condition Monitoring. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79519-1_8.

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Fourati, Aroua, Nabih Feki, Adeline Bourdon, Didier Rémond, Fakher Chaari, and Mohamed Haddar. "Electrical Modeling for Faults Detection Based on Motor Current Signal Analysis and Angular Approach." In Applied Condition Monitoring. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_2.

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Yakoubi, Mohamed Chadi, Walid Najjar, and Hatem Mrad. "Numerical and Experimental Study of the Lubricant Oil Leak Phenomenon on a Metro Traction Motor Gear Box." In Applied Condition Monitoring. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34190-8_32.

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Hocine, Fenineche, and Felkaoui Ahmed. "Electric Motor Bearing Diagnosis Based on Vibration Signal Analysis and Artificial Neural Networks Optimized by the Genetic Algorithm." In Applied Condition Monitoring. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_21.

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Migdał, Wiesław, Jacek Wodecki, Maciej Wuczyński, Paweł Stefaniak, Agnieszka Wyłomańska, and Radosław Zimroz. "Long Term Temperature Data Analysis for Damage Detection in Electric Motor Bearings with Density Modeling and Bhattacharyya Distance." In Applied Condition Monitoring. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11220-2_16.

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Usman, Saifudin, and Muh Anhar. "Condition Monitoring for Induction Motor using Wireless Vibration Monitoring System." In Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023). Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-364-1_105.

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Phuong, Nguyen Cong. "Condition Monitoring for Induction Motor Overload Using Sound." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6385-4_13.

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Conference papers on the topic "Condition Monitoring of DC Motor"

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Siktár, Bálint, György Hegedűs, and József Kakuk. "Relation between the Cutting Force and the Current Consumption of the BLDC Motor in a Hedge Cutter." In 10th International Scientific Conference on Advances in Mechanical Engineering. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-mcnxi8.

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One of the critical aspects in performance optimization of energy efficiency for a Brushless DC (BLDC) motor applied to hedge cutters is that of cutting force versus current consumption. The mechanical cutting force produced during the hedge cutting operation will be presented, here, in a manner relating to the electrical current drawn by the BLDC motor. It indicates that the cutting forces depend on the blade's sharpness, the density of the material, the speed of cutting; and that the current consumed by the motor is directly proportional to the value of this cutting force. The outcome of this study reveals that motor current monitoring performs favorably as an alternative measurement of the resistance of cut and mechanical load in real-time, seeking to enable the implementation of intelligent systems for performance optimization and predicting maintenance. The findings from this will also be beneficial in designing energy-efficient motors for custom-made cutting conditions and thus increase the overall robustness and cost-effective aspects related to operation of hedge cutters. A more accurate representation of this relationship can be refined through further research focusing on material properties and motor control algorithms, allowing for better use of energy and more precise control of horticultural implements. The aim of this paper is to investigate the relation between the different cutting forces occurring during the hedge trimming process and the current consumption of the BLDC motor. The paper will approach this question from a theoretical (calculation and simulation) point of view.
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K, Usha, Saraswathy T D, Harini Sree V S, Sai Kanna R, and Pritha K. "IoT-Based DC Motor Monitoring System." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11020053.

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Mishra, Prajjawal, Sohan Rath, Chandrasekhar Badachi, Souvik Keshri, and Umesh Debnath. "Condition Monitoring of a 3-Phase Induction Motor." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714753.

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Santarpia, Andrea. "Data-Driven Insights for Induction Motor Condition Monitoring." In 2024 PCIC Energy Europe (PCIC Energy). IEEE, 2024. http://dx.doi.org/10.23919/pcicenergy62009.2024.10638162.

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Rahman, Tuan A. Z., Leong Wen Chek, and Baarath Kunjunni. "Metaheuristic-based Feature Selection Algorithm for Electric Motor Condition Monitoring." In 2024 10th International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, 2024. https://doi.org/10.23919/cmd62064.2024.10766164.

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Rahman, Tuan A. Z., Baarath Kunjunni, and Leong Wen Chek. "Evaluation of Feature Extraction Techniques for an Electric Motor Condition Monitoring." In 2024 IEEE 22nd Student Conference on Research and Development (SCOReD). IEEE, 2024. https://doi.org/10.1109/scored64708.2024.10872635.

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Schober, Bernhard, and Uwe Schichler. "Review of IEC 60270 Regarding PD Measurements Under DC Voltage." In 2024 10th International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, 2024. https://doi.org/10.23919/cmd62064.2024.10766170.

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Negishi, Motohiro, Kentaro Akada, Kaoru Takizawa, Hiroaki Miyake, Yasuhiro Tanaka, and Tatsuo Takada. "Space charge accumulation in coating materials for motor windings under DC high voltage." In 2012 IEEE International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, 2012. http://dx.doi.org/10.1109/cmd.2012.6416376.

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Papathanasopoulos, Dimitrios A., Epaminondas D. Mitronikas, Konstantinos N. Giannousakis, and Evangelos S. Dermatas. "An Alternative Approach for Condition Monitoring of Brushless DC Motor Drives." In 2020 International Conference on Electrical Machines (ICEM). IEEE, 2020. http://dx.doi.org/10.1109/icem49940.2020.9271071.

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Zubizarreta-Rodriguez, Jose F., and Shrihari Vasudevan. "Condition monitoring of brushless DC motors with non-stationary dynamic conditions." In 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2014. http://dx.doi.org/10.1109/i2mtc.2014.6860523.

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Reports on the topic "Condition Monitoring of DC Motor"

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Arhin, Stephen, Babin Manandhar, and Adam Gatiba. Influence of Pavement Conditions on Commercial Motor Vehicle Crashes. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2343.

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Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected and analyzed, focusing on CMV routes in DC. The analysis employs binary logistic regression to explore relationships between injury occurrence after a CMV crash and multiple independent variables. Additionally, Artificial Neural Network (ANN) models were developed to classify CMV crash injury severity. Importantly, the inclusion of pavement condition variables (International Roughness Index and Pavement Condition Index) substantially enhanced the accuracy of the logistic regression model, increasing predictability from 0.8% to 41%. The study also demonstrates the potential of Artificial Neural Network models in predicting CMV crash injury severity, achieving an accuracy of 60% and an F-measure of 0.52. These results highlight the importance of considering road pavement conditions in road safety policies and interventions. The study provides valuable insights for policymakers and stakeholders aiming to enhance road safety for CMVs in the District of Columbia and showcases the potential of machine learning techniques in understanding the complex interplay between road conditions and CMV crash occurrences.
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Drive modelling and performance estimation of IPM motor using SVPWM and Six-step Control Strategy. SAE International, 2021. http://dx.doi.org/10.4271/2021-01-0775.

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This paper presents a comprehensive evaluation of the performance of an interior permanent magnet (IPM) traction motor drive, and analyses the impact of different modulation techniques. The most widely used modulation methods in traction motor drives are Space vector modulation (SVPWM), over-modulation, and six-step modulation have been implemented. A two-dimensional electromagnetic finite element model of the motor is co-simulated with a dynamic model of a field-oriented control (FOC) circuit. For accurate tuning of the current controllers, extended complex vector synchronous frame current regulators are employed. The DC-link voltage utilization, harmonics in the output waveforms, torque ripple, iron losses, and AC copper losses are calculated and compared with sinusoidal excitation. Overall, it is concluded that the selection of modulation technique is related to the operating condition and motor speed, and a smooth transition between different modulation techniques is essential to achieve a better performance.
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