Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Duan, Zhihe; Wu, Tonghai; Guo, Shuaiwei; Shao, Tao; Malekian, Reza; Li, Zhixiong

doi:10.1007/s00170-017-1474-8

Cited by 164 publications

(86 citation statements)

References 131 publications

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“…Recently, non-invasive methods have been developed, e.g., acoustic, vibrational, thermal, magnetic methods [11][12][13]. Non-invasive methods are capable of diagnosing early faults without disassembly of the induction motor.…”

Section: Introductionmentioning

confidence: 99%

Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors

Goh

Kim²

2019

Applied Sciences

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When a turn-to-turn short fault occurs in an induction motor, it will be accompanied by vibration and heating, which will have adverse effects on the entire power system. Thus, turn-to-turn short fault diagnosis of the stator is required, and major accidents can be prevented if an inter-turn short circuit (ITSC), which is the early stage of a turn-to-turn short, can be detected. This study reinterprets Park’s vector approach using Direct-Quadrature(D-Q) transformation for the linear separation of ITSCs and proposes an ITSC diagnosis method by defining the magnetic flux linkage pulsation and current change in the event of a turn-to-turn short. It is difficult to diagnose because the turn-to-turn short current change in an ITSC is considerably different from the induction motor loss. Hence, it was found through analysis that when the current change is considered through an analysis of the relationship between inductance and the winding number, the ITSC current becomes slightly smaller than the steady-state current. This was verified using the D-Q synchronous reference frame over time. We proposed a linear separation of the ITSC diagnosis from the steady state by considering the minimum values of the pulsating current as feature points.

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Section: Introductionmentioning

confidence: 99%

Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors

Goh

Kim²

2019

Applied Sciences

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“…Condition monitoring of electric motors was developed for measurement and analysis of diagnostic signals such as acoustic [1,2], thermal [3,4], electric current [5][6][7], and vibration [8][9][10][11][12][13]. Each type of signal has advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…It indicated that the approach was effective for detection of different faults of rolling bearings [9]. Duan et al [10] described the development of condition monitoring of rolling bearings using vibration analysis, acoustic analysis, oil analysis, temperature analysis, and ultrasonic analysis. e authors indicated that multisensors information fusion is the trend of development [10].…”

Section: Introductionmentioning

confidence: 99%

“…Duan et al [10] described the development of condition monitoring of rolling bearings using vibration analysis, acoustic analysis, oil analysis, temperature analysis, and ultrasonic analysis. e authors indicated that multisensors information fusion is the trend of development [10]. Zurita-Millan et al [11] proposed a vibration signal prognosis methodology of the electromechanical system (kinematic chain).…”

Section: Introductionmentioning

confidence: 99%

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Vibration‐Based Fault Diagnosis of Commutator Motor

Głowacz

2018

Shock and Vibration

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This paper presents a study on vibration-based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF-RATIO-50-SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF-RATIO-50-SFC-EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF-RATIO-50-SFC, MSAF-RATIO-50-SFC-EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (TV). The proposed methods have practical application in industries.

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“…As bearing failure is a dynamic process, it is insufficient to know whether there is any fault or the fault type for the preventive maintenance of bearings in engineering applications. Determining the failure evolution process and accurately monitoring the fault severity are paramount for an effective bearing maintenance [4][5][6]. Consequently, the fault severity monitoring of rolling bearings is becoming increasingly important [7,8].…”

Section: Introductionmentioning

confidence: 99%

Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

Chen

Meng

et al. 2018

Applied Sciences

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Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted. Currently, the bearing fault diagnosis method based on time–frequency image (TFI) recognition is attracting increasing attention. This paper contributes to the ongoing investigation by proposing a new approach for the fault severity monitoring of rolling bearings based on the texture feature extraction of sparse TFIs. The first and main step is to obtain accurate TFIs from the vibration signals of rolling bearings. Traditional time–frequency analysis methods have disadvantages such as low resolution and cross-term interference. Therefore, the TFIs obtained cannot satisfactorily express the time–frequency characteristics of bearing vibration signals. To solve this problem, a sparse time–frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) was developed in this paper. Unlike traditional time–frequency analysis methods, the time–frequency analysis model of the STFA-PD method is based on the theory of sparse representation, and is solved using the first-order primal-dual algorithm. For employing the sparse constraint in the frequency domain, the STFA-PD obtains a higher time–frequency resolution and is free from cross-term interference, as the model is based on a linear time–frequency analysis method. The gray level co-occurrence matrix is then employed to extract texture features from the sparse TFIs as input features for classifiers. Vibration signals of rolling bearings with different fault severity degrees are used to validate the proposed approach. The experimental results show that the developed STFA-PD outperforms traditional time–frequency analysis methods in terms of the accuracy and effectiveness for the fault severity monitoring of rolling bearings.

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Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Cited by 164 publications

References 131 publications

Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors

Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors

Vibration‐Based Fault Diagnosis of Commutator Motor

Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

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