Fault detection of rolling element bearings using optimal segmentation of vibrating signals

Popescu, Theodor D.; Aiordăchioaie, Dorel

doi:10.1016/j.ymssp.2018.06.033

Cited by 35 publications

(16 citation statements)

References 19 publications

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“…The research in [4] uses a linear regression model for optimal segmentation and also the study in [5] has a broad discussion about segmentation and abrupt change detection of a signal, including a few algorithms for different scenarios, e.g. change detection algorithm.…”

Section: B Abrupt Changing Point Detectionmentioning

confidence: 99%

Optimised Time for Travelling Wave Fault Locators in the Presence of Different Disturbances Based on Real-World Fault Data

Parsi

Crossley

2021

IEEE Open J. Power Energy

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The real-world travelling wave fault data investigated in this paper indicate disturbances generate unpredictable, non-stationary and random waveforms which may cause maloperation of protection and control elements in a power system including travelling wave fault locators (TWFL). This type of fault locator is directly dependent on the detection of an accurate time of arrival (ToA) of travelling waves (TW) generated by a fault. This detection becomes complicated in the presence of disturbances when their ToAs are detected earlier than the fault TWs. Since travelling waves occur in the high-frequency bands (e.g. >50 kHz), in this paper a capacitor voltage transformer is employed to measure the TW voltage signals; this involves acquiring the current flowing to the ground and removing the lowfrequency components (50/60 Hz). Disturbances create high magnitude pulses in the pre-fault section of a TW fault signal that last for a short time. Therefore, the time when a TWFL starts its computations requires to be optimised so that the effect of the disturbances is eliminated. The analysis techniques mentioned in this paper are based on real-world travelling wave fault data, and the solution uses statistical tools, such as cost function, mean and standard deviation, alongside Digital Signal Processing algorithms.Index Terms--fault location, optimised time, power network faults, power system disturbance, real-world data, short-circuit fault, travelling waves, travelling wave fault location

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Section: B Abrupt Changing Point Detectionmentioning

confidence: 99%

Optimised Time for Travelling Wave Fault Locators in the Presence of Different Disturbances Based on Real-World Fault Data

Parsi

Crossley

2021

IEEE Open J. Power Energy

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“…An N -way tensor X ∈ R I 1 ×I 2 ×•••×I N is rank-one tensor if it can be written as the outer product of N vectors [19], that is, X = a (1) • a (2) •…”

Section: ) Rank-1 Tensormentioning

confidence: 99%

“…As a key component of various rotating machines, the normal operation of rolling element bearings plays a critical role in ensuring the overall safety of the equipment. Bearing failures can easily lead, in time, to machine failure, with costly consequences [1], [2]. Thus, it is of great importance to detect bearing faults to ensure the safety of machines [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…A hybrid way to fault diagnosis of roller bearings under variable speed condition is presented in [9]. A new technique is promoted for changing detection and optimal segmentation of vibrating data obtained during the operation of rolling element bearings by Popescu and Aiordachioaie [2]. Qin [10] explored an appropriate impulsive feature extraction algorithm based on the sparse representation for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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A Tensor-Based Approach for Identification of Multi-Channel Bearing Compound Faults

2019

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Vibration signal analysis is one of the most effective approaches for detecting faults in bearings. A bearing compound-fault signal always consists of multiple signatures and stochastic noise. The separation of multi-fault signals from them is not only crucial but also very challenging. In order to solve this issue, a novel multiple faults detecting technique has been developed based on tensor factorization. The original multi-channel vibration signals are formulated as a 3-way tensor via the temporal signal, spectra, and channel information in a high-dimensional space. The PARAFAC decomposition is used to analyze the tensor model, and then, principal component analysis (PCA) is introduced to find the numbers of the rankone tensor. Finally, the tensor model is solved by alternating least squares (ALSs) approach combined with PCA technique. The performance of the detection method has been proven by simulation analysis of the multi-channel compound faults signals. The experimental results obtained using the developed technique also demonstrated that compound-fault signatures can be effectively and clearly identified.INDEX TERMS PARAFAC, compound-fault, rolling element bearing, fault identification, tensor.

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“…e rolling bearings in hydraulic pumps operate in harsh environments with high pressure and high temperature, leading to operation degradation or even complete shutdown of the entire mechanical system [1,2]. However, the fault representative features are nonlinear and nonstationary and seriously modulated by noise, and the traditional time domain and frequency domain fault diagnosis methods are not efficient to predict bearing faults, especially under changing and complex operating conditions [3,4]. It still remains a challenging task on how to extract robust and representative features from the collected vibration signals for more reliable bearing fault diagnosis effect.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis in the Mixed Domain Based on Crossover‐Mutation Chaotic Particle Swarm

Tongle

Kong

et al. 2021

Complexity

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The classification frameworks for fault diagnosis of rolling element bearings in rotating machinery are mostly based on analysis in a single time-frequency domain, where sensitive features are not completely extracted. To solve this problem, a new fault diagnosis technique is proposed in the mixed domain, based on the crossover-mutation chaotic particle swarm optimization support vector machine. Firstly, fault features are generated using techniques in the time domain, the frequency domain, and the time-frequency domain. Secondly, the weighted maximum relevance minimum redundancy (WMRMR) algorithm is adopted to reduce the dimension of the feature set and to establish the representative feature set. Thirdly, a new crossover-mutation strategy is suggested to reduce the local minima in optimization, and an optimization disturbance is added. Finally, the support vector machine is optimized using the improved chaotic particle swarm to improve fault classification diagnosis. The effectiveness of the proposed new bearing fault diagnostic technique is verified by experimental tests under different bearing conditions. Test results showed that the bearing fault classification accuracy of CMCPSO-SVM in the mixed domain was much higher than those in a single feature domain.

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Fault detection of rolling element bearings using optimal segmentation of vibrating signals

Cited by 35 publications

References 19 publications

Optimised Time for Travelling Wave Fault Locators in the Presence of Different Disturbances Based on Real-World Fault Data

Optimised Time for Travelling Wave Fault Locators in the Presence of Different Disturbances Based on Real-World Fault Data

A Tensor-Based Approach for Identification of Multi-Channel Bearing Compound Faults

Bearing Fault Diagnosis in the Mixed Domain Based on Crossover‐Mutation Chaotic Particle Swarm

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