Academic literature on the topic 'Oil debris sensor'

Purpose The purpose of this study was to perform a complete experimental assessment of a family of oil ferrous wear debris sensor is performed. The family comprised the original sensor and its re-engineered evolution, which is capable of detecting both amount and size of wear debris particles trapped by the sensor and some predefined oil condition properties. Design/methodology/approach In this work, the first step was to perform a design of experiments for the sensor validation. A specially defined test rig was implemented, and different ferrous wear debris was collected. For each sensor, two different tests were performed. The first test was called a “void test”, where quantified amounts of debris were collided with the sensor without oil. The second one was a dynamic test, where the sensor was installed in the test rig and different amounts of wear debris were added at a constant rate. In addition, specific tests related with oil properties detection were studied. Findings The results show excellent correlation of the sensor output signal with the amount of wear debris and a satisfactory detection of debris size in all ranges. Also, the dynamic test presented adequate representativeness, and sensors performed well in this scenario. Practical implications This paper shows the practical implementation of this type of sensor and the usual detection range and rate of detection for different debris size and quantities. Originality/value This work has a great utility for maintenance managers and equipment designers to fully understand the potential of this type of sensor and its suitability for the application required.

Purpose The purpose of this study is to develop a cylindrical capacitive sensor that has the advantages of high resolution, small size and designability and can be easily installed on lubricant pipeline to monitor lubricant oil debris. Design/methodology/approach A theoretical model of the cylindrical capacitive sensor is presented to analyze several parameters’ effectiveness on the performance of sensor. Numerical simulations are then conducted to determine the optimal parameters for preliminary experiments. Experiments are finally carried out to demonstrate the detectability of developed capacitive sensors. Findings It is clear from experimental results that the developed capacitive sensor can monitor the debris in lubricant oil well, and the capacitance values increase almost linearly when the number and size of debris increase. Research limitations/implications There is lot of further work to do to apply the presented method into the application. Especially, it is necessary to consider several factors’ influence on monitoring results. These factors include the flow rate of the lubricant oil, the temperature, the debris distribution and the vibration. Moreover, future work should consider the influence of the oil degradation to the capacitance change and other contaminations (e.g. water and dust). Practical implications This work conducts a feasibility study on application of capacitive sensing principle for detecting debris in aero engine lubricant oil. Originality/value The novelty of the presented capacitance sensor can be summarized into two aspects. One is that the sensor structure is simple and characterized by two coaxial cylinders as electrodes, while conventional capacitive sensors are composed of two parallel plates as electrodes. The other is that sensing mechanism and physical model of the presented sensor is verified and validated by the simulation and experiment.

Hydraulic oil is the key medium for the normal operation of hydraulic machinery, which carries various wear debris. The information reflected by the wear debris can be used to predict the early failure of equipment and achieve predictive maintenance. In order to realize the real-time condition monitoring of hydraulic oil, an impedance debris sensor that can detect inductance and resistance parameters is designed and studied in this paper. The material and size of wear debris can be discriminated based on inductance-resistance detection method. Silicon steel strips and two rectangular channels are designed in the sensor. The silicon steel strips are used to enhance the magnetic field strength, and the double rectangular detection channels can make full use of the magnetic field distribution region, thereby improving the detection sensitivity and throughput of the sensor. The comparison experiment shows that the coils in series are more suitable for the monitoring of wear debris. By comparing and analyzing the direction and the presence or absence of the signal pulses, the debris sensor can detect and distinguish 46 µm iron particles and 110 µm copper particles. This impedance detection method provides a new technical support for the high-precision distinguishing measurement of metal debris. The sensor can not only be used for oil detection in the laboratory, but also can be made into portable oil detection device for machinery health monitoring.

The oil-line electrostatic sensor (OLES) is a new online monitoring technology for wear debris based on the principle of electrostatic induction that has achieved good measurement results under laboratory conditions. However, for practical applications, the utility of the sensor is still unclear. The aim of this work was to investigate in detail the application potential of the electrostatic sensor for wind turbine gearboxes. Firstly, a wear debris recognition method based on the electrostatic sensor with two-probes is proposed. Further, with the wind turbine gearbox bench test, the performance of the electrostatic sensor and the effectiveness of the debris recognition method are comprehensively evaluated. The test demonstrates that the electrostatic sensor is capable of monitoring the debris and indicating the abnormality of the gearbox effectively using the proposed method. Moreover, the test also reveals that the background signal of the electrostatic sensor is related to the oil temperature and oil flow rate, but has no relationship to the working conditions of the gearbox. This research brings the electrostatic sensor closer to practical applications.

Purpose This paper aims to investigate the effect of misalignment on wear of spur gears and on oil degradation using online sensors. Design/methodology/approach The misalignment effect on gears is created through a self-alignment bearing, and is measured using laser alignment system. Several online sensors such as Fe-concentration sensor, moisture sensor, oil condition sensor, oil temperature sensor and metallic particle sensor are installed in the gear test rig to monitor lubricant quality and wear debris in real time to assess gearbox failure. Findings Offset and angular misalignments are detected in both vertical and horizontal planes. The failure of misaligned gear is observed at both the ends and on both the surfaces of the gear teeth. Larger-size ferrous and non-ferrous particles are traced by metallic particle sensor due to gear and seal wear caused by misalignment. Scanning electron microscope (SEM) images examine chuck, spherical and flat platelet particles, and confirm the presence of fatigue (pitting) and adhesion (scuffing) wear mechanism. Energy-dispersive X-ray spectroscopy analysis of SEM particles traces carbon (C) and iron (Fe) elements due to gear failure. Originality/value Gear misalignment is one of the major causes of gearbox failure and the lubricant analysis is as important as wear debris analysis. A reliable online gearbox condition monitoring system is developed by integrating wear and oil analyses for misaligned spur gear pair in contact.

A micro inductance sensor model based on the software of Maxwell to detect the debris in oil is presented. The model is built to study the detecting performance of sensors with different turns and enwinding styles. It can be demonstrated that the planar coil with 15 turns has a better sensitivity to detecting metal debris with about 100 micro meters in size. The solenoid coil with 20 turns has a better performance to detect the micro metal debris. This simulation shows that the detecting performance of a sensor is related to its parameters such as the size, turns, style of enwinding.The optimization of coils for the detection of metal debris with 100 in size is presented at last.

The inductive detection of wear debris in lubrication oil is an effective method to monitor the machine status. As the wear debris is usually micro scale, a micro inductive sensor is always used to detect them in research papers or high-tech products. However, the improvement of detection sensitivity for micro inductive sensors is still a great challenge, especially for early wear debris of 20 μm or smaller diameter. This paper proposes a novel method to improve the detection sensitivity of a micro inductive sensor. Regarding the magnetic powder surrounding the sensor, the magnetic field in the core of the sensor where the wear debris pass through would be enhanced due to the increased relative permeability. Thus, the inductive signal would be improved and the detection sensitivity would be increased. It is found that the inductive signal would linearly increase with increasing the concentration of the magnetic powder and this enhancement would also be effective for wear debris of different sizes. In addition, the detection limit of the micro inductive sensor used in our experiment could be extended to 11 μm wear debris by the proposed method.

Wear is one of the most common failure modes in mechanical machines and the lubricating oil carries a lot of wear information. For monitoring the ferromagnetic debris in oils, a sensor is developed based-on electromagnetic induction principle. The magnetic transient model of sensor coils and particle was established by using Maxwell. The experiment results can reveal the total ferrous debris in an oil sample.

Large wear debris is an important indication of abnormal wear of aviation engines. Analysis of the relationship between particle parameters and the output signal plays an important role to improve the sensor sensitivity. A three-coil simulating sensor model is constructed using APDL finite element program. After analyzing the influence factors to the induced output voltage of the sensor, such as debris material, location, size, exciting current etc, the fitting characteristic curves are obtained, realizing the quantitative analysis. Simulation results show that the sensor model can effectively distinguish between conductive and non conductive, ferromagnetic and non ferromagnetic debris. The induced voltage can reach 10-4 V for the 500 microns ferromagnetic abrasive particles. The characteristic curves provide important basis for further research on sensor structure optimization.

Wear is one of the main factors of machine failure. If abnormal wear was not detected in time during the operation of a mechanical system, it probably leads to catastrophic consequences. The wear debris in the lubricating oil circuit contains much information about equipment wear. Consequently, debris detection is regarded as an effective way to detect mechanical faults. In this paper, an inductive debris sensor based on a high-gradient magnetic field is presented for high-throughput lubricating oil circuits. The excitation coil of the sensor is driven by a constant current to generate a high-gradient magnetic field, and the induction coil is wound around the flow path. When wear debris cuts the magnetic line through the flow path, a corresponding induced voltage is generated. The experimental results show that the sensor output signal is linear with the drive current and the wear debris velocity. In addition, the shortest distance between the particles that the sensor output signals can be completely separated is 25 mm. When the distance is smaller, the induced signals are superimposed.

Online Oil Debris Monitors (ODM) provide a direct, effective and reliable approach to machinery condition monitoring. ODM can be used to monitor the condition of complex machines, such as airplane engines, electric generators, wind turbines, or other machines with oil circulation systems. The principle of the sensor is to detect the quantity and the size of metal particles in the flowing oil. The current available ODM sensors suffer from sensitivity to vibrations, as their electromagnetic response is largely affected by interfering vibrations. This thesis presents a novel structure and algorithms to separate and eliminate the vibration interference. In the new structure, a dual channel system is designed as opposed to previous single channel systems. Three signal processing algorithms have been developed and tested using experimental data from a prototype. They have shown to be effective, as detailed in the thesis.

The current socio-economic and environmental context worldwide, with different actors and needs, requires continued progress towards energy efficiency and environmental improvements in order to create a sustainable future, and this implies a scientific and technologic effort to achieve the proposed goals. Transport by propulsive systems based on reciprocating internal combustion engines (ICE) is one of the major agents affecting future environmental sustainability. Included in the wide research done in this area, one of the options considered is the use of low viscosity oils (LVO) as an option for increasing ICE efficiency. This technology presents a modest contribution to the efficiency target, but the excellent cost-effectiveness ratio and ease of application to current and future vehicle parc are two reasons that has driven towards research into the use of these oils. The low viscosity oils base their contribution to improving energy efficiency by reducing mechanical losses associated with viscous friction in hydrodynamic regime. This in turn reduces energy consumption to operate the system, and it is associated with a reduction of pollutant emissions for the same performance. The hypotheses of application of LVO are well founded, but there are a number of uncertainties surrounding the application of low viscosity oils in MCIA today. On one hand, it is possible to expect a modification of the ICE tribological performance, as well as changes in lubricant performance which ultimately could lead to a reduction in the period of useful life, an early lubrication failure or other consequences difficult to predict. Also, a reduction in viscosity may increase wear production, so there is also an interest in the remote diagnosis of lubricated system status. In this Thesis a concise review of the state-of-the-art has been done applied to ICE tribology and lubricating oils, with special interest in the low viscosity oils development. Then, a series of different studies have been performed to deepen the understanding of oil performance and its influence on ICE wear, supported by a set of physico-chemical analytical techniques applied to diagnose the state of the lubricating oil. The different results obtained show that the application of low viscosity oils in ICE is a viable alternative, since the results obtained in the various tests validate the different hypotheses done, and it opens a line of research possibilities around future enhancements and technology development.
La situación actual a nivel mundial, enmarcada en un contexto socioeconómico y medioambiental complejo, con diferentes actores y necesidades presentes, requiere un avance continuo hacia la eficiencia energética y las mejoras medioambientales de cara a poder crear un futuro sostenible, así como de un esfuerzo científico y tecnológico para poder alcanzar los objetivos propuestos. El transporte mediante sistemas propulsivos basados en motores de combustión interna alternativos (MCIA) es uno de los grandes agentes que afectan a la sostenibilidad medioambiental futura. Dentro de la profunda investigación que se realiza en éste ámbito, una de las opciones estudiadas es la del uso de aceites de baja viscosidad (LVO) como opción para el aumento de la eficiencia de los MCIA. Esta tecnología presenta una aportación modesta al objetivo de eficiencia energética, pero la excelente relación coste-beneficio y la facilidad de aplicación al parque automovilístico actual y futuro son dos razones que han impulsado a la industria hacia la investigación en el uso de estos aceites. Los aceites de baja viscosidad basan su aportación a la mejora de la eficiencia energética en la reducción de las pérdidas mecánicas asociadas a la fricción viscosa en régimen hidrodinámico. Así, se consigue reducir el consumo de energía utilizado para hacer funcionar el sistema, y lleva asociada una reducción de las emisiones contaminantes para el mismo desempeño. La hipótesis de aplicación de los aceites de baja viscosidad están bien fundamentadas, pero existen una serie de incertidumbres alrededor de la aplicación de los aceites de baja viscosidad en MCIA a día de hoy. Por un lado, es posible esperar una modificación del comportamiento tribológico en el propio MCIA, así como una variación del propio comportamiento del lubricante que en último lugar podría provocar una reducción del período de vida útil del mismo, un fallo temprano de lubricación u otras consecuencias difíciles de prever. Además, la bajada de viscosidad puede aumentar el fenómeno de desgaste, por lo que existe también un interés en la cuantificación y diagnóstico de manera continua y remota del estado del sistema lubricado. Así, en esta Tesis se ha realizado un conciso trabajo de revisión del estado del arte de la tribología aplicada a MCIA y de los aceites lubricantes, poniendo especial interés en el desarrollo de la idea de los aceites de baja viscosidad. A continuación, y con el apoyo de un conjunto de técnicas analíticas físico-químicas aplicadas a diagnosticar el estado del aceite lubricante, se han planteado una serie de estudios desde diferentes ámbitos para poder profundizar en el conocimiento del comportamiento del aceite y de su influencia en el desgaste en MCIA. Los diferentes resultados obtenidos señalan que la aplicación de los aceites de baja viscosidad en MCIA es una alternativa viable y exitosa, ya que los resultados obtenidos en los diferentes ensayos realizados validan el comportamiento de esta opción, y abre una línea de posibilidades de investigación alrededor de futuras mejoras y de desarrollo de la tecnología.
La situació actual a nivell mundial, emmarcada en un context socioeconòmic i mediambiental complex, amb diferents actors i necessitats presents, requereix d'un avanç continu cap a l'eficiència energètica i les millores mediambientals de cara a poder crear un futur sostenible, així com d'un esforç científic i tecnològic per poder assolir els objectius proposats. El transport mitjançant sistemes propulsius basats en motors de combustió interna alternatius (MCIA) és un dels grans agents que afecten la sostenibilitat mediambiental futura. Dins de la profunda investigació que es realitza en aquest àmbit, una de les opcions estudiades és la de l'ús d'olis de baixa viscositat (LVO) com a opció per a l'augment de l'eficiència dels MCIA. Aquesta tecnologia presenta una aportació modesta a l'objectiu d'eficiència energètica, però l'excel¿lent relació cost-benefici i la facilitat d'aplicació al parc automobilístic actual i futur són dues raons que han impulsat a la indústria cap a la investigació en l'ús d'aquestos olis. Els olis de baixa viscositat basen la seva aportació a la millora de l'eficiència energètica en la reducció de les pèrdues mecàniques associades a la fricció viscosa en règim hidrodinàmic. Així, s'aconsegueix reduir el consum d'energia utilitzat per fer funcionar el sistema, i porta associada una reducció de les emissions contaminants per a l'obtenció del mateix resultat. Les hipòtesis d'aplicació dels olis de baixa viscositat estan ben fonamentades, però hi ha una sèrie d'incerteses al voltant de l'aplicació dels olis de baixa viscositat en MCIA a dia de hui. D'una banda, és possible esperar una modificació del comportament tribològic en el propi MCIA, així com una variació del propi comportament del lubricant que en últim lloc podria provocar una reducció del període de vida útil d'aquest, una fallada de lubricació primerenca o altres conseqüències difícils de preveure. A més, la baixada de viscositat pot augmentar el fenomen de desgast, pel que existeix també un interès en la quantificació i diagnòstic de manera contínua i remota de l'estat del sistema lubricat. Així, en aquesta Tesi s'ha realitzat un concís treball de revisió de l'estat de l'art de la tribologia aplicada a MCIA i dels olis lubricants, posant especial interès en el desenvolupament de la idea dels olis de baixa viscositat. A continuació, i amb el suport d'un conjunt de tècniques analítiques fisico-químiques aplicades a diagnosticar l'estat de l'oli lubricant, s'han plantejat una sèrie d'estudis des de diferents àmbits per poder aprofundir en el coneixement del comportament de l'oli i de la seva influència en el desgast en MCIA. Els diferents resultats obtinguts assenyalen que l'aplicació dels olis de baixa viscositat en MCIA és una alternativa viable, ja que els resultats obtinguts en els diferents assajos realitzats validen el comportament d'aquesta opció, i obre una línia de possibilitats d'investigació al voltant de futures millores i de desenvolupament de la tecnologia.
Miró Mezquita, G. (2017). Estudio del comportamiento y de la influencia en el desgaste de los aceites lubricantes de baja viscosidad en MCIA [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/78615
TESIS

Bosch Rexroth in Mellansel is manufacturing hydraulic motors and constantly trying to improve their products to reduce downtime for their customers. An important thing to get a reliable system is to know the condition. In a hydraulic motor, it is crucial to determine the particle contamination of the oil to determine the condition. To do so many particle sensors have been tested by Bosch Rexroth but also other related companies during the past years. To this point, no sensor has been performing good enough to replace the ordinary magnetic plug for the laboratory tests at Bosch Rexroth.  The ordinary magnetic plug is based on an openable lid that has a magnet attached to it. The lid is opened to review the particle contamination of the system. To open the lid the motor has to be stopped and a competent person needs to be present to review the particles.To ease the work for the laboratory personal and also getting one step closer to a reliable condition monitoring solution a new idea was coined by employees at Bosch Rexroth. The idea was to use a magnet outside a glass disc and by that be able to detect the particles from outside the motor. Initial testing of the idea had been performed with promising results but more development was needed. The idea has therefore been investigated and developed further in this project. This has been done in parallel with an investigation of state-of-the-art techniques available on the market. The testing showed that the new type of magnet/glass solution was performing well and was able to detect particles of different sizes. Some other interesting options were also found during the investigation of other techniques but the new magnet/glass idea was the most prominent.

碩士
國立中山大學
機械與機電工程學系研究所
106
The operation of mechanical equipment requires the use of a lubrication system. The detection of the quality and contamination of lubricants remains an important part of today''s industry. Our laboratory has previously developed an online automatic detection device for the concentration of iron particles in the oil, so that it can instantly know the operation status of the mechanical equipment and prevent the damage of mechanical equipment at an early stage. The detection principle of the device was the magnetic flux detection method, but the Hall sensor and the detection accuracy of the device were still insufficient and need to be improved. Therefore, this study explored the effect of different Hall sensors on the accuracy of detection. The sensor W135 was selected. Results showed that when the iron particle concentration was 800ppm, the Hall voltage difference could achieve 88mV. Compared with the previous results used the sensor AH49E where the Hall voltage difference was 8mV, its sensitivity was increases by about 11 times. Prior to the detection of the EDM fluid, the calibration was performed using eight different iron particle concentrations. Results showed that the measurement errors were less than 5 ppm. For the non-line detection, when the removal amount was 2000 mg, the Hall voltage value of W135 was 83 mV, which was about 5.18 times higher than that of AH49E (16 mV). For on-line detection, the Hall voltage difference of W135 was 80mV, which is 19 times higher than the 4.2mV of AH49E. Results showed that the W135 Hall sensor was much more sensitive than the AH49E. Finally, the ferrous debris in the EDM fluid was analyzed to improve the accuracy and stability of the device. This study also improved the display unit of the detection device to make it more suitable for intelligent manufacturing engineering. After compiled the microcontroller “Ardunio”, it matched with the LCD panel, so that the iron particle concentration value of the oil could be instantly displayed in the smart phone using the Bluetooth device to instantly monitor the quality of the oil. The operator could simply observe the wear condition of the mechanical equipment.

In this paper, a gas turbine bearing wear monitoring method based on the magnetic plug inductance sensor is presented. Using the induced magnetic field of the magnetic pulse generated by the inductance coil, this method is applied to measure ferromagnetic wear debris in the oil, and the condition of bearing wear can be monitored and predicted on line. To estimate the mass of different particle size of ferromagnetic debris, the sample databases of debris mass were built, the oil capturing experiment was conducted, and the mapping model for the voltage signal of the sensor and the captured accumulation of ferromagnetic wear debris based on the BP (Error Back Propagation) neural network was established. Moreover, the kernel method was used to calculate the voltage distribution of the debris sensor in the step time of wear prediction, and the mean value and confidence boundary value of the signals in the expected step time were obtained. Moreover, the prediction model of the bearing wear was established by a linear regression method to predict the mass of ferromagnetic wear debris generated by bearing wear. Finally, a lubricating oil debris detection system was designed, and the bearing wear test was conducted on the bearing wear testing rig. The results showed that the monitoring method can continuously monitor and dynamically predict the condition of bearing wear online with the advantages of stability and automatic purifying lubricant oil.

Magnetic chip detectors, vibration monitoring devices, and spectrographic oil analysis typically do not detect bearing distress until the bearing is in the latter stages of failure. Now, through the innovation of digital signal processing technology and a breakthrough in inductive sensors, it is possible to provide several months of advance notice on a bearing failure. Unlike magnetic chip detectors, this technology has the capability to track the shedding of both magnetic and nonmagnetic debris from the bearing. It is a true prognostic sensor that detects the first indications of a bearing spall and continues to track, in real-time, the quantities of wear debris being generated by the bearing. The data collected from the sensor can be changed into information such that with a particle trend graph it can clearly be seen when the bearing should be taken out of service prior to a turbine failure and the possibility of expensive secondary damage.

Development of robust in-flight prognostics or diagnostics for oil wetted gas turbine engine components will play a critical role in improving aircraft engine reliability and maintainability. Real-time algorithms for predicting and detecting bearing and gear failures are currently being developed in parallel with emerging flight-capable sensor technologies including in-line oil debris/condition monitors, and vibration analysis MEMS. These advanced prognostic/diagnostic algorithms utilize intelligent data fusion architectures to optimally combine sensor data, with probabilistic component models to achieve the best decisions on the overall health of oil-wetted components. By utilizing a combination of health monitoring data and model-based techniques, a comprehensive component prognostic capability can be achieved throughout a components life, using model-based estimates when no diagnostic indicators are present and monitored features such as oil debris and vibration at later stages when failure indications are detectable. Implementation of these oil-wetted component prognostic modules will be illustrated in this paper using bearing and gearbox test stand run-to-failure data.

Advanced warning from arrays of on-line sensors to trigger more in-depth laboratory testing is now possible because the development phase of “smart” sensors has matured. There has been a need to develop reliable, affordable on-line instrumentation (sensors) that provides information on both lubricant and machinery condition (wear) supports critical systems requirements, addresses remote applications that are difficult to physically sample routinely, and that reduces manpower needs. The monitoring of specific oil related parameters like, alkalinity, acidity, and contaminants such as ferrous and non-ferrous debris, water, coolant and soot, with no human involvement, will be reviewed in this paper. Sensor technology includes Magnetometry and TAN Delta. They have been evaluated on test rig and on-engine trials to assess the improvement of sensitivity, insulation, electronics, connectors, and specialised software with interpretive algorithms to suit specific applications. Sensors will supplant some, but not all routine testing, thereby changing the Oil Analysis landscape.

Condition monitoring technique has been widely applied in Maritime to ensure safe operation and minimise unscheduled downtime. However, in practice, ship operators need to assure that a failure mode is indeed monitored by the sensor intended for it, and the sensor has sufficient accuracy and precision for its purpose. Additionally, for a reliable condition monitoring technique, issues such as sensors degradation or drift that will reduce the data quality over time must be addressed. All these require that ship owners to select a monitoring system with the best suitable sensors technology while is economically viable. In this paper, tunnel thruster was used as a case study to demonstrate the basic approach to develop a reliable condition monitoring technique through Failure Mode, Effects and Criticality Analysis (FMECA). Based on failure modes, four types of condition monitoring techniques were identified including Vibration Monitoring, Acoustic Emission Monitoring, Wear Debris /Water in Oil Monitoring, and Thermal Monitoring, where vibration monitoring is discussed in detail as an example for defining the sensor specification. For a reliable condition monitoring technique, prediction of sensor reliability will be especially useful in the situation where sensors systems can degrade over time in service. Using temperature sensors as an example, a Bayesian network (BN) modeling approach has been carried out for assessing sensor reliability affected by aging.

Condition-based maintenance (CBM) is an emerging technology which seeks to develop sensors and processing systems aimed at monitoring the operation of complex machinery such as turbine engines, rotor craft drive trains, or industrial equipment. The goal of CBM systems is to determine the state of the equipment (i.e., the mechanical health and status), and to predict the remaining useful life for the system being monitored. The success of such systems depends upon a number of factors including: (1) the ability to design or use robust sensors for measuring relevant phenomena such as vibration, acoustic spectra, infrared emissions, oil debris, etc.; (2) real time processing of the sensor data to extract useful information (such as features or data characteristics) in a noisy environment and to detect parametric changes which might be indicative of impending failure conditions; (3) fusion of multi-sensor data to obtain improved information beyond that available to a single sensor; (4) micro and macro level models which predict the temporal evolution of failure phenomena; and finally, (5) the capability to perform automated approximate reasoning to interpret the results of the sensor measurements, processed data, and model predictions in the context of an operational environment. The latter capability is the focus of this paper. Although numerous techniques have emerged from the discipline of artificial intelligence for automated reasoning (e.g., rule-based expert systems, blackboard systems, case-based reasoning, neural networks, etc.), none of these techniques are able to satisfy all of the requirements for reasoning about condition-based maintenance. This paper provides an assessment of automated reasoning techniques for CBM and identifies a particular problem for CBM, namely, the ability to reason with negative information (viz., data which by it’s absence is indicative of mechanical status and health). A general architecture is introduced for CBM automated reasoning, which hierarchically combines implicit and explicit reasoning techniques. Initial experiments with fuzzy logic are also described.