Intelligent Diesel Engine Fault Diagnosis Method Based on Time-Frequency-Nonconvex Robust Principal Component Analysis

Wang, Fang; Wang, Lida; Wen, Yufang; Ha, Fei; Jia, Litao; Jiao, Wenbin

doi:10.1109/icsmd57530.2022.10058265

Cited by 1 publication

(1 citation statement)

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“…The fault diagnosis of rotating machinery usually takes the time-domain, frequency-domain, and time-frequency domain features extracted from the sampled device-produced signals first, and then a classifier is adopted to achieve the classification of faults occurred based on the features [11][12][13]. In [14], a flexible time-frequency analysis method is proposed to isolate and identify multiple faults occurring in the different components of rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Frequency-Tracking Method Based on Interpolated Kalman Filter Using Vibration and Surface Noise

Li,

Wan,

Pan

et al. 2024

Mathematical Problems in Engineering

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The variation of frequency is a significant indicator of the operation status of rotating machinery. Generally, the frequency is extracted and tracked in real-time based on the vibration signal produced by the rotating machinery. However, various interferences generated from contact sampling and transmitting result in difficulty in obtaining the frequency correctly in real-time from the vibration. To solve this problem, this paper presents an interpolated Kalman filter (IKF) based on the vibration and surface noise signals for real-time frequency tracking. First, the cross-correlation operation is performed on the vibration and surface noise signals sampled synchronously to enhance the energy concentration. After that, a frequency search procedure is carried out to calculate the input of the tracking task. Finally, an IKF-based frequency lock procedure is applied to eliminate the interferences and track the frequency in real time. Besides, a correction procedure is added to prevent the measurement process from tracking the frequency incorrectly. The performance of the proposed method is verified by the experiments based on an ARM-based test bench using standard signals and actual test signals.

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