Detection of common motor bearing faults using frequency-domain vibration signals and a neural network based approach

Li, Bo; Goddu, G.; Chow, Mo-Yuen

doi:10.1109/acc.1998.702983

Cited by 32 publications

(5 citation statements)

References 2 publications

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“…These basic frequencies defined above can be used to detect the possible faults in bearing or gears. For example, if the defects happen on the raceway of a rolling bearing, the BPOF f or BPIF f will be generated when a roller hits this defective raceway each time [5]. After the bearing defect is detected, some vibration-based features need to be extracted in order to trace the bearing health degradation.…”

Section: Vibration Data Analysismentioning

confidence: 99%

Research on Condition Monitoring of Bearing Health Using Vibration Data

Kang¹,

Zhao

2012

AMM

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A reliable condition monitoring system is very useful in a wide range of industries to detect the occurrence of incipient defects so as to prevent machinery performance degradation, malfunction and sudden failure. Among the rotating machinery, many mechanical problems are attributed due to bearing failures. So implementing condition monitoring for bearing is critically needed. Considering that most research for condition monitoring only focus on detecting the existing fault, this paper add degradation tendency prognostics into the condition monitoring process. The kernel of bearing condition monitoring method presented in this paper is related to condition features extraction and remaining useful life prediction. The former is realized by the comprehensive vibration analysis for specific fault frequencies. The latter is achieved by adaptive neuron-fuzzy inference system based on extracted degradation signal. For illustration purpose, a bearing case from NASA data repository is used to validate the feasibility of the proposed method. The result indicates that the performance degradation of bearing can be effectively monitored and the predicted remaining useful life with 5.6% relative error can be the important reference for maintenance decision making.

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Section: Vibration Data Analysismentioning

confidence: 99%

Research on Condition Monitoring of Bearing Health Using Vibration Data

Kang¹,

Zhao

2012

AMM

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“…Mo-Yuen Chow et al [16] concluded that the size of training data directly affects the accuracy of the AI system. Bo Li et al (1998) showed that FFT values of a vibration signal spectrum can be used as relevant features of the vibration dataset [17]. The authors of [4,16] proved that optimizing network parameters like learning rate, momentum, and neuron size is important for increasing accuracy.…”

Section: Introductionmentioning

confidence: 99%

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft

Wisal

2023

Sensors

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Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms have been developed to study rotor imbalance. The classifier algorithms, though simple to use, lack the flexibility to be used efficiently for both low and high numbers of classes. Therefore, a robust multiclass prediction algorithm is needed to efficiently classify the rotor imbalance problem during runtime and avoid the problem’s escalation to failure. In this work, a new deep learning (DL) algorithm was developed for detecting the unbalance of a rotating shaft for both binary and multiclass identification. The model was developed by utilizing the depth and efficacy of ResNet and the feature extraction property of Convolutional Neural Network (CNN). The new algorithm outperforms both ResNet and CNN. Accelerometer data collected by a vibration sensor were used to train the algorithm. This time series data were preprocessed to extract important vibration signatures such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). STFT, being a feature-rich characteristic, performs better on our model. Two types of analyses were carried out: (i) balanced vs. unbalanced case detection (two output classes) and (ii) the level of unbalance detection (five output classes). The developed model gave a testing accuracy of 99.23% for the two-class classification and 95.15% for the multilevel unbalance classification. The results suggest that the proposed deep learning framework is robust for both binary and multiclass classification problems. This study provides a robust framework for detecting shaft unbalance of rotating machinery and can serve as a real-time fault detection mechanism in industrial applications.

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“…These features were compared with features computed using the fast Fourier transform (FFT) as a suitable choice for real-time implementation [13]. Li et al introduced a method for motor-bearing fault detection using frequency domain vibration signals and ANN [14]. In this method, the acquired vibration signals in the time domain were converted into the frequency domain using the FFT method.…”

Section: Introductionmentioning

confidence: 99%

Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals

Ahmed

Nandi

2022

Entropy

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As failures of rolling bearings lead to major failures in rotating machines, recent vibration-based rolling bearing fault diagnosis techniques are focused on obtaining useful fault features from the huge collection of raw data. However, too many features reduce the classification accuracy and increase the computation time. This paper proposes an effective feature selection technique based on intrinsic dimension estimation of compressively sampled vibration signals. First, compressive sampling (CS) is used to get compressed measurements from the collected raw vibration signals. Then, a global dimension estimator, the geodesic minimal spanning tree (GMST), is employed to compute the minimal number of features needed to represent efficiently the compressively sampled signals. Finally, a feature selection process, combining the stochastic proximity embedding (SPE) and the neighbourhood component analysis (NCA), is used to select fewer features for bearing fault diagnosis. With regression analysis-based predictive modelling technique and the multinomial logistic regression (MLR) classifier, the selected features are assessed in two case studies of rolling bearings vibration signals under different working loads. The experimental results demonstrate that the proposed method can successfully select fewer features, with which the MLR-based trained model achieves high classification accuracy and significantly reduced computation times compared to published research.

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Detection of common motor bearing faults using frequency-domain vibration signals and a neural network based approach

Cited by 32 publications

References 2 publications

Research on Condition Monitoring of Bearing Health Using Vibration Data

Research on Condition Monitoring of Bearing Health Using Vibration Data

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft

Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals

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