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Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.


Papers
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Journal ArticleDOI
Xinping Yan1, C.H. Zhao1, Z.Y. Lu1, Xincong Zhou1, H.L. Xiao1 
TL;DR: The progress in the application of IT in the authors' laboratory shows that the combination of information technology and oil monitoring can increase the speed of oil analysis, manage the information conveniently and obtain analysis conclusion more precisely in relation to practical application.

67 citations

Journal ArticleDOI
TL;DR: In this article, an adaptive parameter identifier uses a generalized gradient descent algorithm to compute real-time estimates of system parameters (e.g., capacitance, inductance, parasitic resistance) in arbitrary switching power electronics systems.
Abstract: This paper presents the design, implementation, and experimental validation of a method for fault prognosis for power electronics systems using an adaptive parameter identification approach. The adaptive parameter identifier uses a generalized gradient descent algorithm to compute real-time estimates of system parameters (e.g., capacitance, inductance, parasitic resistance) in arbitrary switching power electronics systems. These estimates can be used to monitor the overall health of a power electronics system and to predict when faults are more likely to occur. Moreover, the estimates can be used to tune control loops that rely on the system parameter values. The parameter identification algorithm is general in that it can be applied to a broad class of systems based on switching power converters. We present a real-time experimental validation of the proposed fault prognosis method on a 3 kW solar photovoltaic interleaved boost dc–dc converter system for tracking changes in passive component values. The proposed fault prognosis method enables a flexible and scalable solution for condition monitoring and fault prediction in power electronics systems.

67 citations

Patent
Shintaro Yokoyama1, Kouichi Kojima1
20 May 1997
TL;DR: In this paper, a driving condition-monitoring apparatus for an automotive vehicle monitors the driving condition of a driver of the automotive vehicle and determines whether the driver's driving condition is abnormal or not based on the data generated by the monitoring system.
Abstract: A driving condition-monitoring apparatus for an automotive vehicle monitors a driving condition of a driver of the automotive vehicle. At least one of behavior of the vehicle, a driving operation of the driver, and at least one condition of the driver is detected to thereby generate driving condition-indicative data indicative of the driving condition of the driver. It is determined whether the driving condition of the driver is abnormal, based on the driving condition-indicative data generated. When it is not determined that the driving condition of the driver is abnormal, a degree of normality of the driving condition of the driver is determined by inputting a plurality of pieces of the driving condition-indicative data to a neural network. At least one of warning and control of the vehicle is carried out depending on a result of the determination as to whether the driving condition of the driver is abnormal and the degree of normality of the driving condition of the driver.

67 citations

Journal ArticleDOI
TL;DR: A study that uses principal component analysis to reduce dimensionality of the feature space and to get an optimal subspace for machine fault classification and has a good potential for application in practice is presented.
Abstract: Feature extraction is a key issue to machine condition monitoring and fault diagnosis. The features must contain the necessary discriminative information for the fault classifier to have any chance of accurate classification. This paper presents a study that uses principal component analysis to reduce dimensionality of the feature space and to get an optimal subspace for machine fault classification. Industrial gearbox vibration signals measured from different operating conditions are analyzed using the above method. The experimental results indicate that the method extracts diagnostic information effectively for gear fault classification and has a good potential for application in practice.

67 citations

Journal ArticleDOI
TL;DR: In this paper, a new automated technique for testing voltage source inverter-fed squirrel-cage induction machines at a standstill for rotor faults is proposed, which uses the inverter to excite the machine with a pulsating field at a number of angular positions to observe the variation in the impedance pattern due to broken rotor bars, whenever the motor is stopped.
Abstract: It is difficult to apply conventional online motor current signature analysis techniques for diagnosis of rotor faults for closed-loop induction motor drives for many applications due to the masking effect of the feedback current controller and/or variable frequency or load operation. Relying solely on traditional offline inspection techniques during regular maintenance does not allow frequent monitoring of rotor problems and is inconvenient since it requires rotor disassembly and/or manual rotor rotation. In this paper, a new automated technique for testing voltage source inverter-fed squirrel-cage induction machines at a standstill for rotor faults is proposed. The main concept is to use the inverter to excite the machine with a pulsating field at a number of angular positions to observe the variation in the impedance pattern due to broken rotor bars, whenever the motor is stopped. An experimental study on a 7.5-hp induction motor verifies that broken bars can be detected with high sensitivity and reliability. It will be shown that the proposed method can provide automated and reliable assessment of rotor condition frequently without motor disassembly, manual rotation, or additional instrumentation. The proposed test can also provide rotor quality assessment independent of variations in motor or load operating conditions since it is a standstill test.

67 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023164
2022413
2021798
2020927
2019936
2018906