Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines

Wu, Jian-Da; Chen, Jien-Chen

doi:10.1016/j.ndteint.2005.09.002

Cited by 143 publications

(72 citation statements)

References 20 publications

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“…Alternatively, TFD combines the information in both the time and frequency domains to analyze the nonstationary signal in bearing fault diagnosis [21]. Several techniques can be used to construct the TFD, such as short time Fourier transform (STFT), discrete wavelet transform (DWT), continuous wavelet transform (CWT) [22,23], and WPT [24]. In this study, the STFT is utilized to construct the TFD of the envelope signal ( ) as written in what follows:…”

Section: Ifr and Ifcf Estimation From Tfd And Ridge Extractionmentioning

confidence: 99%

Fault Diagnosis of Motor Bearing by Analyzing a Video Clip

Wang

Liu

et al. 2016

Mathematical Problems in Engineering

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Conventional bearing fault diagnosis methods require specialized instruments to acquire signals that can reflect the health condition of the bearing. For instance, an accelerometer is used to acquire vibration signals, whereas an encoder is used to measure motor shaft speed. This study proposes a new method for simplifying the instruments for motor bearing fault diagnosis. Specifically, a video clip recording of a running bearing system is captured using a cellphone that is equipped with a camera and a microphone. The recorded video is subsequently analyzed to obtain the instantaneous frequency of rotation (IFR). The instantaneous fault characteristic frequency (IFCF) of the defective bearing is obtained by analyzing the sound signal that is recorded by the microphone. The fault characteristic order is calculated by dividing IFCF by IFR to identify the fault type of the bearing. The effectiveness and robustness of the proposed method are verified by a series of experiments. This study provides a simple, flexible, and effective solution for motor bearing fault diagnosis. Given that the signals are gathered using an affordable and accessible cellphone, the proposed method is proven suitable for diagnosing the health conditions of bearing systems that are located in remote areas where specialized instruments are unavailable or limited.

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Section: Ifr and Ifcf Estimation From Tfd And Ridge Extractionmentioning

confidence: 99%

Fault Diagnosis of Motor Bearing by Analyzing a Video Clip

Wang

Liu

et al. 2016

Mathematical Problems in Engineering

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“…The wavelet transform technique has special benefits for describing signals at various localization levels in time, in addition to frequency domains [15,19]. Discrete wavelet transform is obtained by discretizing the scaling and shifting parameters in continuous wavelet transform.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location

Yang

Wang

2015

Entropy

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Abstract:In actual application, sensors are prone to failure because of harsh environments, battery drain, and sensor aging. Sensor fault location is an important step for follow-up sensor fault detection. In this paper, two new multi-level wavelet Shannon entropies (multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy) are defined. They take full advantage of sensor fault frequency distribution and energy distribution across multi-subband in wavelet domain. Based on the multi-level wavelet Shannon entropy, a method is proposed for single sensor fault location. The method firstly uses a criterion of maximum energy-to-Shannon entropy ratio to select the appropriate wavelet base for signal analysis. Then multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are used to locate the fault. The method is validated using practical chemical gas concentration data from a gas sensor array. Compared with wavelet time Shannon entropy and wavelet energy Shannon entropy, the experimental results demonstrate that the proposed method can achieve accurate location of a single sensor fault and has good anti-noise ability. The proposed method is feasible and effective for single-sensor fault location.

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“…Fourier transforms (FT) and fast Fourier transform (FFT) analysis can be utilized to examine the signal in the regularity area. The application premise of discrete Fourier is that it is supposed that the signal is both stationary and periodic [9]. However, the diesel engine is a composite system in which the combined shock is excited by a complicated mechanism motion and combustion.…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction of Diesel Engine Based on Bispectrum Image Fractal Dimension

Zhang

Liu

et al. 2018

Chin. J. Mech. Eng.

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Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early fault signals are mostly weak energy signals, and time domain or frequency domain features will be overwhelmed by strong background noise. In order consistent features to be extracted that accurately represent the state of the engine, bispectrum estimation is used to analyze the nonlinearity, non-Gaussianity and quadratic phase coupling (QPC) information of the engine vibration signals under different conditions. Digital image processing and fractal theory is used to extract the fractal features of the bispectrum pictures. The outcomes demonstrate that the diesel engine vibration signal bispectrum under different working conditions shows an obvious differences and the most complicated bispectrum is in the normal state. The fractal dimension of various invalid signs is novel and diverse fractal parameters were utilized to separate and characterize them. The value of the fractal dimension is consistent with the non-Gaussian intensity of the signal, so it can be used as an eigenvalue of fault diagnosis, and also be used as a non-Gaussian signal strength indicator. Consequently, a symptomatic approach in view of the hypothetical outcome is inferred and checked by the examination of vibration signals from the diesel motor. The proposed research provides the basis for on-line monitoring and diagnosis of valve train faults.

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Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines

Cited by 143 publications

References 20 publications

Fault Diagnosis of Motor Bearing by Analyzing a Video Clip

Fault Diagnosis of Motor Bearing by Analyzing a Video Clip

Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location

Fault Feature Extraction of Diesel Engine Based on Bispectrum Image Fractal Dimension

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