Academic literature on the topic 'Artificial intelligence deep learning fault detection and diagnosis condition monitoring'
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Journal articles on the topic "Artificial intelligence deep learning fault detection and diagnosis condition monitoring"
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Khan, Muhammad Amir, Bilal Asad, Karolina Kudelina, Toomas Vaimann, and Ants Kallaste. "The Bearing Faults Detection Methods for Electrical Machines—The State of the Art." Energies 16, no. 1 (2022): 296. http://dx.doi.org/10.3390/en16010296.
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Electrical machines are prone to faults and failures and demand incessant monitoring for their confined and reliable operations. A failure in electrical machines may cause unexpected interruptions and require a timely inspection of abnormal conditions in rotating electric machines. This article aims to summarize an up-to-date overview of all types of bearing faults diagnostic techniques by subdividing them into different categories. Different fault detection and diagnosis (FDD) techniques are discussed briefly for prognosis of numerous bearing faults that frequently occur in rotating machines. Conventional approaches, statistical approaches, and artificial intelligence-based architectures such as machine learning and deep learning are discussed summarily for the diagnosis of bearing faults that frequently arise in revolving electrical machines. The most advanced trends for diagnoses of frequent bearing faults based on intelligence and novel applications are reviewed. Future research directions that are helpful to enhance the performance of conventional, statistical, and artificial intelligence (machine learning, deep learning) and novel approaches are well addressed and provide hints for future work.
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Gültekin, Özgür, Eyup Cinar, Kemal Özkan, and Ahmet Yazıcı. "Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence." Sensors 22, no. 9 (2022): 3208. http://dx.doi.org/10.3390/s22093208.
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Early fault detection and real-time condition monitoring systems have become quite significant for today’s modern industrial systems. In a high volume of manufacturing facilities, fleets of equipment are expected to operate uninterrupted for days or weeks. Any unplanned interruptions to equipment uptime could jeopardize manufacturers’ cycle time, capacity, and, most significantly, credibility for their customers. With the help of smart manufacturing technologies, companies have started to develop and integrate fault detection and classification systems where end-to-end constant monitoring of equipment is facilitated, and smart algorithms are adapted for the early generation of fault alarms and classification. This paper proposes a generic real-time fault diagnosis and condition monitoring system utilizing edge artificial intelligence (edge AI) and a data distributor open source middleware platform called FIWARE. The implemented system architecture is flexible and includes interfaces that can be easily expanded for various devices. This work demonstrates it for condition monitoring of autonomous transfer vehicle (ATV) equipment targeting a smart factory use case. The system is verified in a designated industrial model environment in a lab with a single ATV operation. The anomaly conditions of the ATV are diagnosed by a deep learning-based fault diagnosis method performed in the Edge AI unit, and the results are transferred to the data storage via a data pipeline setup. The proposed system’s Edge AI solution for the ATV use case provides significant real-time performance. The network bandwidth requirement and total elapsed data transfer time have been reduced by 43 and 37 times, respectively. The proposed system successfully enables real-time monitoring of ATV fault conditions and expands to a fleet of equipment in a real manufacturing facility.
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Barcelos, Andre S., and Antonio J. Marques Cardoso. "Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms." Energies 14, no. 9 (2021): 2509. http://dx.doi.org/10.3390/en14092509.
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Artificial intelligence algorithms and vibration signature monitoring are recurrent approaches to perform early bearing damage identification in induction motors. This approach is unfeasible in most industrial applications because these machines are unable to perform their nominal functions under damaged conditions. In addition, many machines are installed at inaccessible sites or their housing prevents the setting of new sensors. Otherwise, current signature monitoring is available in most industrial machines because the devices that control, supply and protect these systems use the stator current. Another significant advantage is that the stator phases lose symmetry in bearing damaged conditions and, therefore, are multiple independent sources. Thus, this paper introduces a new approach based on fractional wavelet denoising and a deep learning algorithm to perform a bearing damage diagnosis from stator currents. Several convolutional neural networks extract features from multiple sources to perform supervised learning. An information fusion (IF) algorithm then creates a new feature set and performs the classification. Furthermore, this paper introduces a new method to achieve positive unlabeled learning. The flattened layer of several feature maps inputs the fuzzy c-means algorithm to perform a novelty detection instead of clusterization in a dynamic IF context. Experimental and on-site tests are reported with promising results.
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Katta, Pradeep, Karunanithi Kandasamy, Raja Soosaimarian Peter Raj, Ramesh Subramanian, and Chandrasekar Perumal. "Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 11, no. 3 (2023): 349–65. http://dx.doi.org/10.14201/adcaij.28435.
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The recent improvements related to the area of electric locomotive, power electronics, assembly processes and manufacturing of machines have increased the robustness and reliability of induction motors. Regardless of the increased availability, the application of induction motors in many fields alleges the need for operating state supervision and condition monitoring. In other words, fault identification at the initial stage helps make appropriate control decisions, influencing product quality as well as providing safety. Inspired by these demands, this work proposes a regression based modeling for the analysis of performance in induction motors. In this approach, the feature extraction process is combined with classification for efficient fault detection. Deep Belief Network (DBN) stacked with multiple Restricted Boltzmann Machine (RBM) is exploited for the robust diagnosis of faults with the adoption of training process. The influences of harmonics over induction motors are identified and the losses are mitigated. The simulation of the suggested approach and its comparison with traditional approaches are executed. An overall accuracy of 99.5% is obtained which in turn proves the efficiency of DBN in detecting faults.
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Khan, M. A. Masud. "AI AND MACHINE LEARNING IN TRANSFORMER FAULT DIAGNOSIS: A SYSTEMATIC REVIEW." American Journal of Advanced Technology and Engineering Solutions 1, no. 01 (2025): 290–318. https://doi.org/10.63125/sxb17553.
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Power transformers are critical components of electrical power systems, and their failure can lead to severe operational disruptions, financial losses, and safety hazards. Traditional transformer fault diagnosis techniques, such as dissolved gas analysis (DGA), partial discharge (PD) monitoring, and frequency response analysis (FRA), rely heavily on expert knowledge and rule-based frameworks, making them prone to inaccuracies and inconsistencies. Recent advancements in artificial intelligence (AI) and machine learning (ML) have introduced data-driven methodologies that enhance fault detection, classification, and predictive maintenance by automating feature extraction and improving diagnostic accuracy. This systematic review, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, evaluates 107 peer-reviewed studies published between 2010 and 2024, assessing the role of AI and ML in transformer fault diagnosis. The findings highlight that deep learning models, particularly convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, achieve superior fault classification accuracy compared to conventional methods, with some models surpassing 95% accuracy in real-world applications. Hybrid AI models, such as ANN-SVM combinations and reinforcement learning-based optimizations, further enhance diagnostic reliability by mitigating data inconsistencies and optimizing fault classification strategies. AI-driven predictive maintenance models demonstrate substantial improvements in transformer health monitoring by shifting from traditional time-based maintenance to condition-based strategies, reducing unexpected failures by up to 40%. Additionally, multi-sensor integration techniques, including wireless sensor networks (WSNs) and IoT-enabled monitoring systems, enhance fault detection accuracy by fusing real-time data from different diagnostic modalities. However, the review also identifies challenges related to AI model interpretability, dataset limitations, and deployment scalability, which need to be addressed for broader industrial adoption. Overall, this study underscores the transformative role of AI in improving transformer fault detection, classification, and predictive analytics, paving the way for more efficient and automated power grid management.
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Nausheen, Pathan, Pathan Shadabkhan, Shaikh Naeem, and Shaikh Saba. "From Automation to Optimization: A Review of AI in Manufacturing Systems." Recent Trends in Automation and Automobile Engineering 6, no. 3 (2023): 16–25. https://doi.org/10.5281/zenodo.10159490.
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<i>Recent advancements in the field of artificial intelligence have already started to integrate into our everyday lives. Although AI development is still in its early stages, it has demonstrated the ability to surpass human intelligence in certain areas (such as AlphaGo by DeepMind). This suggests significant potentialfor its broader implementation across various industries. In particular, the growing interestin Industry 4.0, which aims to modernizetraditional manufacturing, has sparked increasedexploration of AI applications in related sectors.However, AI does have limitations that need to be addressed before widespread adoption. This has led to ongoing research into the fusion of artificial intelligence with other engineering disciplines, such as precision engineering and manufacturing. This outline means to sum up a portion of the remarkable accomplishments involving simulated intelligence in powerful and productive assembling businesses, fully intent on reforming fabricating offices.</i>
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Mallikarjuna, P. B., M. Sreenatha, S. Manjunath, and Niranjan C. Kundur. "Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques." Journal of Intelligent Systems 30, no. 1 (2020): 258–72. http://dx.doi.org/10.1515/jisys-2019-0237.
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Abstract Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long short term memory (BLSTM)) are proposed to classify the condition of gearbox into good or bad. These models are applied on aircraft gearbox vibration data in both time and frequency domain. A publicly available aircraft gearbox vibration dataset is used to evaluate the performance of proposed models. The results proved that accuracy achieved by LSTM and BLSTM are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain. Also, to show the superiority of proposed models for aircraft gearbox fault diagnosis, performance is compared with classical machine learning models.
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Patro, Sidharth, Trupti Ranjan Mahapatra, Sushmita Dash, and Vikram Kishore Murty. "Artificial intelligence techniques for fault assessment in laminated composite structure: a review." E3S Web of Conferences 309 (2021): 01083. http://dx.doi.org/10.1051/e3sconf/202130901083.
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There is a continuous quest in the research community for superior and more accurate methodology for fault diagnosis and condition monitoring of diverse composite structure. This is because, these structures suffer from various nonlinear mode of failures while in service those are recognised as delamination, voids, matrix crack etc. Early detection of failures is what the most research mainly aims at. In this regard, the implementation of Artificial Intelligence (AI) techniques has been proved to be a versatile method for damage assessment. The collective inevitable use of composite materials in various high-performance engineering industries requires preliminary testing (detection, location, and quantification) for damage to these materials in order to improve their integrity and order. The present paper aims to bring out a concise review on various methodologies employed for damage/fault detection in composite materials with a special emphasis on supervised and unsupervised machine learning techniques. The major observations are outlined with an objective to put forward a broad perspective of the state of art related to laminated composite structural heath monitoring.
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Al-Haddad, Luttfi A., and Alaa Abdulhady Jaber. "An Intelligent Fault Diagnosis Approach for Multirotor UAVs Based on Deep Neural Network of Multi-Resolution Transform Features." Drones 7, no. 2 (2023): 82. http://dx.doi.org/10.3390/drones7020082.
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As a modern technological trend, unmanned aerial vehicles (UAVs) are extensively employed in various applications. The core purpose of condition monitoring systems, proactive fault diagnosis, is essential in ensuring UAV safety in these applications. In this research, adaptive health monitoring systems perform blade balancing fault diagnosis and classification. There seems to be a bidirectional unpredictability within each, and this paper proposes a hybrid-based transformed discrete wavelet and a multi-hidden-layer deep neural network (DNN) scheme to compensate for it. Wide-scale, high-quality, and comprehensive soft-labeled data are extracted from a selected hovering quad-copter incorporated with an accelerometer sensor via experimental work. A data-driven intelligent diagnostic strategy was investigated. Statistical characteristics of non-stationary six-leveled multi-resolution analysis in three axes are acquired. Two important feature selection methods were adopted to minimize computing time and improve classification accuracy when progressed into an artificial intelligence (AI) model for fault diagnosis. The suggested approach offers exceptional potential: the fault detection system identifies and predicts faults accurately as the resulting 91% classification accuracy exceeds current state-of-the-art fault diagnosis strategies. The proposed model demonstrated operational applicability on any multirotor UAV of choice.
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Yan, Jingyi, Soroush Senemmar, and Jie Zhang. "Inter-turn Short Circuit Fault Diagnosis and Severity Estimation for Wind Turbine Generators." Journal of Physics: Conference Series 2767, no. 3 (2024): 032021. http://dx.doi.org/10.1088/1742-6596/2767/3/032021.
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Abstract While preventive maintenance is crucial in wind turbine operation, conventional condition monitoring systems face limitations in terms of cost and complexity when compared to innovative signal processing techniques and artificial intelligence. In this paper, a cascading deep learning framework is proposed for the monitoring of generator winding conditions, specifically to promptly detect and identify inter-turn short circuit faults and estimate their severity in real time. This framework encompasses the processing of high-resolution current signal samples, coupled with the extraction of current signal features in both time and frequency domains, achieved through discrete wavelet transform. By leveraging long short-term memory recurrent neural networks, our aim is to establish a cost-efficient and reliable condition monitoring system for wind turbine generators. Numeral experiments show an over 97% accuracy for fault diagnosis and severity estimation. More specifically, with the intrinsic feature provided by wavelet transform, the faults can be 100% identified by the diagnosis model.
Dissertations / Theses on the topic "Artificial intelligence deep learning fault detection and diagnosis condition monitoring"
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Cariño, Corrales Jesús Adolfo. "Fault detection and identification methodology under an incremental learning framework applied to industrial electromechanical systems." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/458451.
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Condition Based Maintenance is a program that recommends actions based on the information collected and interpreted through condition monitoring and has become accepted since a decade ago by the industry as a key factor to avoiding expensive unplanned machine stoppages and reaching high production ratios. Among the condition based maintenance strategies, data-driven fault diagnosis methodologies have gained increased attention because of the high performance and widen range of applicability due to less restrictive constrains in comparison to other approaches. Therefore, an increased effort is been made to develop reliable methodologies that could diagnose multiple known faults on a machine with initial applications in controlled environments like laboratory test benches.
However, applying those methods to industry applications still represent an ongoing challenge due to the multiple limitations involved and the high reliability and robustness required. One of the most important challenges in the industrial sector refers to the management of unexpected events, in respect of how to detect new faults or anomalies in the machine. In addition, the information initially available of the monitored industrial machine is usually limited to the healthy condition, therefore is not only necessary to detect these new scenarios but also incorporate this information to the initial base knowledge.
In this regard, this thesis present a series of complementary methodologies that leads to the implementation of a fault detection and identification system capable to detect multiple faults and new scenarios of industrial electromechanical machines under an incremental learning framework to include the new scenarios detected to the initial base knowledge while achieving a high performance and generalization capabilities. Initially, a methodology to increase the performance of novelty detection models to detect unexpected events in electromechanical system is proposed. Then, a methodology to implement a sequential fault detection and identification system composed by a novelty detection and a fault diagnosis stages with high accuracy is proposed. Finally, two different methodologies are proposed to provide the sequential fault detection and identification system the capacity to include new scenarios to the base knowledge. The proposed methodologies have been validated by means of experimental data of laboratory test benches and industrial electromechanical systems.<br>"Mantenimiento basado en la condición" es un programa que recomienda una serie de medidas preventivas basadas en la información recopilada e interpretada mediante el constante monitoreo de la condición de la maquinaria y ha sido aceptado desde hace una década por la industria como un factor clave para evitar paradas no planificadas de la maquinaria y alcanzar altos índices de producción. Entre las estrategias de mantenimiento basadas en la condición, las metodologías de diagnóstico de fallos basadas en datos han recibido mucha atención debido al alto rendimiento y amplio rango de aplicabilidad, esto se debe que cuentan con menos limitaciones en comparación con otros enfoques. Por lo tanto, se ha hecho un mayor esfuerzo para desarrollar metodologías fiables que puedan diagnosticar múltiples fallos conocidas en una máquina, siendo aplicado inicialmente en entornos controlados como bancadas de laboratorio. Sin embargo, aplicar estos métodos en la industria sigue representando un desafío debido a las múltiples limitaciones implicadas y la alta fiabilidad y robustez requeridas. Uno de los desafíos más importantes en el sector industrial consiste en la gestión de eventos inesperados, específicamente en cómo detectar nuevos fallos o anomalías máquina. Además, la información inicialmente disponible de la máquina industrial monitorizada se limita generalmente al estado sano, por lo tanto, no sólo es necesario detectar estos nuevos escenarios, sino también incorporar esta información al conocimiento base inicial. En este sentido, esta tesis presenta una serie de metodologías complementarias que conducen a la implementación de un sistema de detección e identificación de fallos capaz de detectar múltiples fallos y nuevos escenarios de máquinas electromecánicas industriales en un marco de aprendizaje incremental para incluir los nuevos escenarios detectados al conocimiento base inicial manteniendo un alto rendimiento y capacidades de generalización. Inicialmente, se propone una metodología para aumentar el rendimiento de los modelos de detección de novedad para detectar eventos inesperados en el sistema electromecánico. Después, se propone una metodología para implementar un sistema secuencial de detección e identificación de fallas con alta precisión compuesto por una etapa de detección de novedades y otra de diagnóstico de fallos. Finalmente, se proponen dos metodologías diferentes para proporcionar al sistema secuencial de detección e identificación de fallas la capacidad de incluir nuevos escenarios al conocimiento base. Las metodologías propuestas han sido validadas mediante datos experimentales de bancadas de laboratorio y sistemas electromecánicos industriales.
Book chapters on the topic "Artificial intelligence deep learning fault detection and diagnosis condition monitoring"
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Gadi, Anil Lokesh. "Intelligent vehicle health monitoring through engine data, artificial intelligence, and machine learning." In Deep Science Publishing. Deep Science Publishing, 2025. https://doi.org/10.70593/978-93-49307-21-6_8.
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Data rejection and filtration are required in this step to remove outliers and noise, to get a realistic picture of normal behaviour. The output of a health monitoring system is usually a numerical quantity or an indicator that quantifies the condition of the monitored system's component or subsystem. Different conditions can be represented by different values of such indicators. These features capture higher-level information in the sensor data. The parameters acting as condition indicators for faults are identified and monitored to detect, identify, and characterise faults by studying anomalies and trends. Diagnostic processes allow the rapid determination of specific components that need to be replaced during maintenance. Prognostic processes enable the prediction of the residual life of components by analysing trends in historical observations. A scheme capable of performing fault detection and identification has to be developed first. In case faults are identified, isolation schemes should indicate the degraded subsystem or part of the system which is affected by the fault. Finally, a set of different nudges has to be identified and assessed, regarding the more or less strong deviation from expected performance that is introduced by the fault and its progression. This assessment has to be performed either by making direct use of a generic degradation model or by employing machine learning techniques.
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Toma, Rafia Nishat, Yangde Gao, and Jong-Myon Kim. "Data-Driven Fault Classification of Induction Motor Based on Recurrence Plot and Deep Convolution Neural Network." In Machine Learning and Artificial Intelligence. IOS Press, 2022. http://dx.doi.org/10.3233/faia220425.
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Condition monitoring becomes an integral part of the industrial manufacturing system to ensure a safe working environment and reduce the cost of maintenance. Involving deep learning techniques in fault diagnosis methods not only increases the accuracy and reliability of the system but also reduces the operation time and hassle of the manual feature extraction process. In this paper, a complete framework for fault classification is introduced by using the vibration signals of bearings containing normal and faulty conditions. Firstly, the frequency spectrums of the time-series signals are generated with FFT and transformed the 1-D signal into 2-D images with the recurrence plots (RP) algorithm. Finally, a deep CNN model is designed to classify the bearing conditions with the extracted high-level features from the RP-based image dataset. The images show a distinct pattern in every bearing condition and the CNN model can achieve 99.24% accuracy to classify three different bearing conditions. The image classification-based fault diagnosis approach is automated and eliminates the disadvantages of the manual feature extraction process. The generated images with RP were also trained with three predefined CNN models to verify the effectiveness of the fault patterns. Finally, the comparative analysis demonstrates that the proposed method outperforms other researchers’ approaches both in terms of classification accuracy and computational cost.
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Dong, Yu, Li Niu, Yanfeng Bai, Luyang Wang, and Yan Liu. "A Study on the Diagnosis of the Working Conditions of a Traveling Beam Pumping Unit Based on Artificial Intelligence." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241204.
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In order to solve the problem of low accuracy of traditional indicator diagram recognition methods, the research on condition diagnosis of beam pumping units based on artificial intelligence is proposed. Through the application of deep learning convolutional neural network in the field of image recognition, this paper proposes a convolutional neural network model based on LeNet, and realizes the automatic recognition of indicator diagrams. The model built by the research institute takes 15 common downhole working conditions of the pumping unit into consideration while simplifying the model structure, and introduces the Dropout layer and local response normalization layer to prevent the model from over fitting and improve the generalization ability of the model. The experimental results show that the model not only has a fast convergence rate, but also has an average diagnostic accuracy of 94.68% It meets the diagnostic accuracy requirements of pumping unit condition detection. Conclusion: This study provides a basis for the construction of pumping unit well condition intelligent monitoring and early warning system, which is of great significance to the construction of intelligent oilfield and the efficient production of oilfield.
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Boobalan, M., and B. Bommirani. "Integrating Internet of Things IoT for Real Time Data Driven Operational Decision Making." In Digital Transformation Strategies for Achieving Operational Excellence and Business Resilience. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552821-07.
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The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has revolutionized real-time data-driven operational decision-making across various sectors, enhancing efficiency, accuracy, and predictive capabilities. This book chapter explores the intersection of IoT and AI, focusing on the application of machine learning, deep learning, and edge computing for real-time anomaly detection, fault diagnosis, and predictive analytics. With the exponential growth of data generated by IoT devices, the need for advanced analytical techniques to process and interpret this data in real-time has become paramount. AI-driven approaches enable the continuous monitoring of IoT systems, identifying anomalies, classifying faults, and predicting potential failures before they disrupt operations. The chapter delves into the challenges and solutions in deploying AI models for IoT systems, emphasizing the role of edge AI, federated learning, and transfer learning in enhancing privacy, scalability, and computational efficiency. By integrating these advanced AI techniques with IoT frameworks, industries can achieve predictive maintenance, optimize resource allocation, and improve operational resilience. This comprehensive analysis provides valuable insights into the future of smart systems, where real-time decision-making is empowered by AI and IoT convergence.
Conference papers on the topic "Artificial intelligence deep learning fault detection and diagnosis condition monitoring"
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Aydemir, Gürkan. "Deep Learning Based Spectrum Compression Algorithm for Rotating Machinery Condition Monitoring." In ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/smasis2018-8137.
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In the new data intensive world, predictive maintenance has become a central issue for the modern industrial plants. Monitoring of electric machinery is one of the most important challenges in predictive maintenance. Adaptive manufacturing processes/plants may be possible through the monitored conditions. In this respect, several attempts have been made to utilize deep learning algorithms for rotating machinery fault detection and diagnosis. Among them, deep autoencoders are very popular, because of their denoising effect. They are also implemented in electric machinery fault diagnostics in order to obtain lower order representation of signals. However, none of these efforts regard the autoencoders as compression units. Bearing in mind that spectra of vibration and current signals that are collected from electric machinery are critical instruments for detection and diagnosis of their faults, we propose that deep stacked autoencoder can be utilized as spectrum compression units. The performance of the proposed strategy are assessed using a bearing data set in three ways: (1)Rule-based classifiers are implemented on raw and compressed-decompressed spectrum and their performance are compared. (2) It is shown that the several machine learning classifiers such as support vector machines, artificial neural networks and k-nearest neighbour classifiers on compressed-decompressed spectrum achieves the performance of them on raw data. (3) A multi-layer perceptron (MLP) classifier is implemented on the low dimensional representation and it is demonstrated that the strategy of employing the same autoencoder as pretraining of feature extraction module cannot outperform the performance of this MLP classifier.
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Mendanha, Vinicius Faria Costa, André Pereira Marques, and Cacilda de Jesus Ribeiro. "Studies on applications of Artificial Intelligence in medium and high voltage circuit breakers." In VI Seven International Multidisciplinary Congress. Seven Congress, 2024. http://dx.doi.org/10.56238/sevenvimulti2024-074.
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Artificial Intelligence (AI) has played an important role in several engineering applications, including preventive maintenance and fault detection in high-voltage electrical equipment. Among them, medium and high-voltage circuit breakers stand out, which are strategic components, used not only in maneuvers, but also in protection against overcurrents and short circuits in electrical power systems. Failures in these equipment can lead to significant interruptions in the power supply, sometimes causing great economic and social losses, as well as risks to the safety of facilities. In this sense, the objective of this work is to present different scientific studies on AI applied to medium and high-voltage circuit breakers, aiming at the analysis and comparisons between them. The justification for these studies is evidenced by the need to identify mechanical and electrical faults early, minimizing unplanned downtime and costs associated with the corrective maintenance of these equipment. The methodology adopted is based on available scientific studies with selection and analysis of cases on the application of AI in diagnosing incipient faults of medium and high-voltage circuit breakers. The results demonstrate the efficiency of integrating AI algorithms. They present different methods, such as signal processing techniques, for example: Wavelet Transform and Improved Empirical Mode Decomposition Energy Entropy; Machine Learning, namely: Principal Component Analysis (PCA), K-means , Random Forest and Support Vector Machine (SVM); and Deep Learning, such as: AlexNet Network and Autoencoder , to extract relevant features from the vibration and voltage signals of these equipment. Therefore, this work highlights the importance of applying Artificial Intelligence aiming at innovations in the area of Maintenance Engineering. Given the challenges and perspectives in the area, we propose complements with studies that use methods that deal well with little data and can be used for more constant monitoring of the operating status of medium and high voltage circuit breakers. Furthermore, these tools must be able to identify when the equipment has undergone intervention and whether its condition has improved, as well as present failure predictions based on its history, since the application of AI techniques shows promise in the early detection of failures, preventive maintenance and improvement of the operational efficiency of this important equipment for the electrical power system.
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Golyadkin, Maksim, Vitaliy Pozdnyakov, Leonid Zhukov, and Ilya Makarov. "SensorSCAN: Self-supervised learning and deep clustering for fault diagnosis in chemical processes (Abstract Reprint)." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/951.
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Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts in order to be used in predictive modeling. However, manual annotation of large amounts of data can be difficult in industrial settings. In this paper, we propose SensorSCAN, a novel method for unsupervised fault detection and diagnosis, designed for industrial chemical process monitoring. We demonstrate our model's performance on two publicly available datasets of the Tennessee Eastman Process with various faults. The results show that our method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed FPR) and effectively detects most of the process faults without expert annotation. Moreover, we show that the model fine-tuned on a small fraction of labeled data nearly reaches the performance of a SOTA model trained on the full dataset. We also demonstrate that our method is suitable for real-world applications where the number of faults is not known in advance. The code is available at https://github.com/AIRI-Institute/sensorscan
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Jiang, B. T., J. Zhou, and X. B. Huang. "Artificial Neural Networks in Condition Monitoring and Fault Diagnosis of Nuclear Power Plants: A Concise Review." In 2020 International Conference on Nuclear Engineering collocated with the ASME 2020 Power Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icone2020-16334.
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Abstract Artificial neural networks (ANNs) are recognized for their good properties in solving the non-linear classification problem. Especially, ANNs and their latest advancements in deep learning (DL) are blooming in artificial intelligence (AI) fields in the past few years. They have recently proven their abilities to handle some complex fault diagnosis problems. In the context of these backgrounds, this paper provides a concise review on the applications of ANNs to condition monitoring and fault diagnosis (CMFD) of nuclear power plants (NPPs). Firstly, a brief description of basic principle of ANNs are given. Then, a number of studies reported in both the journals and conferences are reviewed. These studies are divided into two categories according the application types of ANNs: shallow ANNs and deep ANNs. Finally, the conclusions and trends developed in the future are summarized.
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M. Raghavan, Shaiju, Arun Palatel, and Jayaraj Simon. "Artificial Intelligence Based Gas Turbine Compressor Wash: A Predictive Approach." In ASME 2019 Gas Turbine India Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gtindia2019-2434.
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Abstract Deep penetration of renewable energy generation has led to increased periods of operation of industrial gas turbines under part load conditions. The performance fault diagnosis of gas turbine module components such as compressor, turbine, and the combustion chamber is a difficult task for such non-design operating conditions. Hence operational data-based performance health monitoring system is a requirement of gas turbine owners or users. The system must be capable to identify the degradation of gas turbine components, having substantial impact on the performance of the module in any possible operating condition. On time identification of degraded components will reduce the cost of operation, ensure service availability and obtain maximum performance from the turbine. This paper illustrates the application of advanced machine learning techniques to the performance analysis, fault diagnosis and prediction of future performance of gas turbine compressor. Compressor fouling is a primary cause of gas turbine performance deterioration, which accounts for 70% to 85% of the performance loss. The first section of the paper focuses on the residual generation of critical parameters of the compressor. The residual of the critical parameters can be calculated by comparing compressor model output with actual plant parameters. The trained artificial neural network (ANN) classifier uses residual of critical parameters to identify the fast rate of compressor fouling in the early stage. Statistical analysis can estimate the future performance of the compressor from the residual of critical parameters. The predicted values of compressor performance are useful for the planning of offline compressor wash, which in turn improve performance, reliability, and availability of gas turbine module. The residuals can also measure the effectiveness of compressor wash and assess the performance after machine overhauling.
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Rahman, Md Arifur, Suhaima Jamal, and Hossein Taheri. "A Deep LSTM-Sliding Window Model for Real-Time Monitoring of Railroad Conditions Using Distributed Acoustic Sensing (DAS)." In 2024 Joint Rail Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/jrc2024-124137.
Full textAbstract:
Abstract Ensuring safety remains the paramount concern within railway systems. While extensive research has been conducted on multiple facets of train safety, the ongoing challenge lies in the real-time monitoring and timely detection of defects, including their occurrence, causes, and severity. Optical fiber cable has been proven to sense long-distance condition monitoring by using optical time domain reflectometry (OTDR). Distributed Acoustic Sensing (DAS) uses fiber optic cables along the track to detect any anomaly indicator such as vibration-based defective features. DAS systems can collect data over long distances. DAS became an excellent solution for real-time condition monitoring due to their high-speed data transmission capabilities, sensing certain mechanical and operational properties, such as strain, vibration, temperature, and pressure, which made the optical cables applicable for real-time structural condition monitoring. Conventional monitoring methods need time, physical inspection, scheduling, and higher financial involvement, while a knowledge-based method contains realtime monitoring, analysis, prediction, and classification of any occurrences at any remote distance from the monitoring station. The crack, rock fail, broken fail, flat wheel, rail condition, and track bed condition estimate from the DAS data require massive data analytics with an intelligence interface. Meanwhile, using sliding windows, machine learning, and deep learning tools, a data-driven intelligent method for fault detection has become an ideal technology among researchers. Fault diagnosis methods based on data-driven algorithms can identify failure types at the inspection site and provide a predictive failure plan to the maintenance team, which leads to increased safety, reliability, and profitability, as well as an improvement in the overall implementation of the data-driven predictive algorithms in the fields. This paper explores railway tracks’ structural health and condition monitoring using DAS data extracted from a High Tonnage Loop (HTL)-fiber optic bed In MxV Rail facilities (Pueblo, CO) and applying a Deep Long-Short Term Memory-Sliding Window (DLSTM-SW) model, that achieved a condition detection accuracy higher than 97% with a swift data processing time which led the model to the application of real-time monitoring of the remote condition of railroad. The findings of this article include automatically labeling each of the railroad’s distributed points or locations, defining the condition such as Defective Location (DL), Non-Defective Location (NDL), and Train Position (TP) along the fiber cable length of the railroad. In addition, as DAS generates a huge amount of data, it takes higher time for data processing, filtering, and feature extraction with traditional machine learning or deep learning methods, while the processed DLSTM-SW model takes only a few seconds which makes the model applicable for real-time monitoring.