Topic
Condition monitoring
About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.
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03 Oct 2004
TL;DR: This work presents the use of multiple sensor modalities in order to perform traffic analysis for health monitoring of transportation infrastructure and testbeds containing video and seismic sensors giving complementary information about vehicles are described.
Abstract: This work presents the use of multiple sensor modalities in order to perform traffic analysis for health monitoring of transportation infrastructure. In particular, testbeds containing video and seismic sensors giving complementary information about vehicles are described. Computer vision algorithms are used to detect and track the vehicles and extract their properties. This information is combined with the data from seismic sensors for robust classification of vehicles. Experimental results obtained with our testbeds are described.
61 citations
TL;DR: A robust RUL prediction method based on constrained Kalman filter is proposed that models the CM signals subject to a set of inequality constraints so that satisfactory prediction accuracy can be achieved regardless of the noise level of signal evolution.
61 citations
TL;DR: In this article, a very low rotational-speed slewing bearing (1-4.5 rpm) without artificial fault was used to detect outlier race fault and rolling element fault.
Abstract: There have been extensive studies on vibration based condition monitoring, prognosis of rotating element bearings; and reviews of the methods on how to identify bearing fault and predict the final failure reported widely in literature. The investigated bearings commonly discussed in the literatures were run in moderate and high rotating speed, and damages were artificially introduced e.g. with artificial crack or seeded defect. This paper deals with very low rotational-speed slewing bearing (1–4.5 rpm) without artificial fault. Two real vibration data were utilized, namely data collected from lab slewing bearing subject to accelerated life test and from a sheet metal company. Empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) were applied in both lab slewing bearing data and real case data. Outer race fault (BPFO) and rolling element fault (BSF) frequencies of slewing bearing can be identified. However, these fault frequencies could not be identified using fast Fourier transform (FFT).
61 citations
10 Jul 2006
TL;DR: In this article, the radial electromagnetic force distribution along the air gap, which is the main source of vibration, is calculated and developed into double Fourier series in space and time.
Abstract: This paper presents a method for determining the signatures of electrical faults in the airgap force distribution and vibration pattern of induction machines. The monitoring of faults is achieved through measurement of the vibrations of the stator frame. The radial electromagnetic force distribution along the airgap, which is the main source of vibration, is calculated and developed into double Fourier series in space and time. Finite element simulations of faulty and healthy machines are performed; they show that the electromagnetic force distribution is a sensible parameter to the changes in the machine condition. The computations show the existence of low-frequency and low-order force distributions acting on the stator of the electrical machine when it is working under fault conditions. The simulation results are corroborated by vibration measurements on an induction motor with implemented broken bars and an inter-turn short circuit. The measurements and simulations show that low-frequency components of the vibrations can be used as identifiable signatures for condition monitoring of induction motors. The determination of the vibration frequency corresponding to a given electric fault in a given machine can be achieved through numerical simulations of the magnetic field and electromagnetic forces in the cross-section of the machine, without need for complex structural analysis.
61 citations
TL;DR: In order to effectively diagnose faults for rotating machinery in the variable rotating speed, a novel diagnosis method is proposed based on time-frequency analysis techniques, the automatic feature extraction method, and fuzzy inference.
Abstract: In order to effectively diagnose faults for rotating machinery in the variable rotating speed, a novel diagnosis method is proposed based on time-frequency analysis techniques, the automatic feature extraction method, and fuzzy inference. The diagnosis sensitivities of three time-frequency analysis methods, namely, the short-time Fourier transform (STFT), wavelet analysis (WA), and the pseudo-Wigner-Ville distribution (PWVD), are investigated for condition diagnosis of rotating machinery. In the case of the bearing diagnosis, the diagnosis sensitivity of the PWVD was found to be highest. An extraction method for instantaneous feature spectrum is proposed using the relative crossing information (RCI), by which the feature spectrum from time-frequency distribution can be automatically extracted by a computer in order to identify among the conditions of a machine. The symptom parameters are also defined in the frequency domain using the feature spectrum extracted by the RCI. The synthetic symptom parameters can be obtained by the least squares mapping (LSM) technique to increase the diagnosis sensitivity of the symptom parameters. Based on the above studies, a fuzzy diagnosis method using sequential inference and possibility theory was also proposed, by which the conditions of machinery can be well identified sequentially. Practical examples of diagnosis for a roller bearing are given in order to verify the effectiveness of the approaches proposed in this paper.
61 citations