Development of a generic rotating machinery fault diagnosis approach insensitive to machine speed and support type

Nembhard, Adrian D.; Sinha, Jyoti; Yunusa‐Kaltungo, Akilu

doi:10.1016/j.jsv.2014.10.033

Cited by 30 publications

(27 citation statements)

References 21 publications

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“…The test rig for current study as shown in Figure 1 is located in the Dynamics Laboratory at The University of Manchester (UK). In order to foster direct comparison between previous studies [2][3][4][5][6][7] and current one, the experimental rig used is quite similar to that used in previous studies [2][3][4][5][6][7] with regards to component location i.e motor, bearings (Bg1 -Bg4), couplers (C1 and C2) and sizes in discs (125mm diameter and 14mm thick) and shaft (1000 mm long, 500 mm short, and both having 20 mm diameter). However, as opposed to the rigid flange bearing mounting of the anti-friction bearing in earlier studies, the current study used 4 flexible springs connection thus increasing foundation flexibility as can be seen in Figure 2.…”

Section: Experimental Rig and Fault Simulationmentioning

confidence: 99%

“…A number of the fault diagnosis (FD) approaches for differentiation of various machine operating conditions (healthy and faulty) with varying degree of foundation flexibilities, changing speeds as well as data combination has been investigated in recent studies [1][2][3][4][5][6][7]. These studies considered various FD techniques in order to obtain an efficient and reliable system.…”

Section: Introductionmentioning

confidence: 99%

“…In a bid for an improved method where diagnosis of an extensive range of faults encountered by rotating machines can be achieved, Luwei et al [1] optimised the unified multi-speed approach, by fusing acceleration and velocity features (dFAVF) from time and frequency domain parameters where good fault classification was achieved during diagnosis. However, these earlier analyses [2][3][4][5][6][7] were carried out using acceleration data while all studies were done with machine operating below the first critical speed.…”

Section: Introductionmentioning

confidence: 99%

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Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

et al. 2018

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Earlier research studies have suggested the unified vibration-based approach for fault diagnosis (FD) in identical machines with different foundation flexibilities and multi-rotating speeds. Intially the acceleration-based features were used for this approach then further work optimised the approach by combining acceleration and velocity features from vibration data for analysis. However the optimised approach was only tested on the identical machines rotating at different speeds below the machine’s first critical speed. The current paper tends to observe the optimised approach when applied to a test rig operating below and above the machine’s first critical speed.

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Section: Experimental Rig and Fault Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

et al. 2018

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“…As machines are becoming more efficient and automatic than ever before, it is more significant to have a safe and sound operation [2]. Meanwhile, rotating speed oscillation usually occurs during machine operation which can accelerate the damage on machines [3]. Therefore, condition monitoring systems are adopted to collect the massive amount of data from monitored machines [4,5].…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering

Qian¹,

Li²,

Wang³

et al. 2018

J. vibroeng.

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Traditional intelligent fault diagnosis methods take advantage of diagnostic expertise but are labor-intensive and time-consuming. Among various unsupervised feature extraction methods, sparse filtering computes fast and has less hyperparameters. However, the standard sparse filtering has poor generalization ability and the extracted features are not so discriminative by only constraining the sparsity of the feature matrix. Therefore, an improved sparse filtering with L1 regularization (L1SF) is proposed to improve the generalization ability by improving the sparsity of the weight matrix, which can extract more discriminative features. Based on Fourier transformation (FFT), L1SF, softmax regression, a new three-stage intelligent fault diagnosis method of rotating machinery is developed. It first transforms time-domain samples into frequency-domain samples by FFT, then extracts features in L1-regularized sparse filtering and finally identifies the health condition in softmax regression. Meanwhile, we propose employing different activation functions in the optimization of L1SF and feedforward for considering their different requirements of the non-saturating and anti-noise properties. Furthermore, the effectiveness of the proposed method is verified by a bearing dataset and a gearbox dataset respectively. Through comparisons with the standard sparse filtering and L2-regularized sparse filtering, the superiority of the proposed method is verified. Finally, an interpretation of the weight matrix is given and two useful sparse properties of weight matrix are defined, which explain the effectiveness of L1SF. . His current research interests include rotating machinery fault diagnosis and mechanical signal and information processing.

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“…Hence, it is of great significance to research feature extraction methods for incipient fault prognosis and guaranteeing the reliability of the mechanical system [1]. In the past decades, many algorithms are proposed in the area of mechanical intelligent diagnosis [2][3][4]. However, as researchers found that the rotating machine is a complicated and nonlinear system, it has been proven that the slight fault feature is usually overwhelmed by heavy background noise, which makes it difficult to detect the weak feature in the early failure of the rotating machine.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Entropy EMD Thresholding for Periodic Fault Signal Enhancement in Rotating Machine

Dang

Jia

Wang

et al. 2017

Shock and Vibration

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Since the slight fault feature of incipient fault is usually polluted by heavy background noise, it is difficult to extract the weak feature signal in rotating machine. As an adaptive decomposing technique, empirical mode decomposition (EMD) based denoising methods have a good effect on the feature separation and noise elimination. However, for rotating machine with poor working environment, the components attributed to noise might have higher amplitudes, which restrict the efficiency of noise reduction in current EMD-based denoising methods. Therefore, a probabilistic entropy EMD thresholding algorithm for periodic fault signal enhancement in rotating machine is proposed in this paper. In this method, the entropy threshold of each IMF is constructed instead of the threshold applied to sampling points of each IMF directly, which overcomes the shortcoming of the denoising effect limited by larger amplitude noise reservation and smaller amplitude feature signal reduction in the current denoising methods. Meanwhile, in order to make the amplitudes of all the IMF reduce in a smooth way, a multiscale thresholding algorithm based on quantile statistics to provide probability indexes is presented. Engineering application demonstrates that the proposed method is effective in the noise reduction and fault feature enhancement in the rotating machine.

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Development of a generic rotating machinery fault diagnosis approach insensitive to machine speed and support type

Cited by 30 publications

References 21 publications

Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering

Probabilistic Entropy EMD Thresholding for Periodic Fault Signal Enhancement in Rotating Machine

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