Diagnosis model for bearing faults in rotating machinery by using vibration signals and binary logistic regression

Abdelrhman, Ahmed M.; Ying, Lim; Ali, Yasir Hassan; Ahmad, Iftikhar; Georgantopoulou, Christina G.; Nor, Fethma M.; Kurniawan, Denni

doi:10.1063/5.0017221

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…However, for more complex problems, an optimised ANN is more accurate and powerful for classification tasks [12]. In addition, multinomial logistic regression (MLR) has been utilized in bearing fault diagnosis [23], which involves the generation of probability distributions for input features and calculation of maximum likelihood functions to identify the most appropriate fitting model [24]. Pandya et al performed a comparative study between MLR, SVM, and ANN and concluded that an MLR model has achieved the highest accuracy for REB fault diagnosis [25].…”

Section: Introductionmentioning

confidence: 99%

A Generalised Intelligent Bearing Fault Diagnosis Model Based on a Two-Stage Approach

Kiakojouri,

Lu,

Mirring

et al. 2024

Machines

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This paper introduces a two-stage intelligent fault diagnosis model for rolling element bearings (REBs) aimed at overcoming the challenge of limited real-world vibration training data. In this study, bearing characteristic frequencies (BCFs) extracted from a novel hybrid method combining cepstrum pre-whitening (CPW) and high-pass filtering developed by the authors’ group are used as input features, and a two-stage approach is taken to develop an intelligent REB fault detect and diagnosis model. In the first stage, various machine learning (ML) methods, including support vector machine (SVM), multinomial logistic regressions (MLR), and artificial neural networks (ANN), are evaluated to identify faulty bearings from healthy ones. The best-performing ML model is selected for this stage. In the second stage, a similar evaluation is conducted to find the most suitable ML technique for bearing fault classification. The model is trained and validated using vibration data from an EU Clean Sky2 I2BS project (An EU Clean Sky 2 project ‘Integrated Intelligent Bearing Systems’ collaborated between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and tested on datasets from Case Western Reserve University (CWRU) and the US Society for Machinery Failure Prevention Technology (MFPT). The results show that the two-stage model, using an SVM with a polynomial kernel function in Stage-1 and an ANN with one hidden layer and 0.05 dropout rate in Stage-2, can successfully detect bearing conditions in both test datasets and perform better than the results in literature without the requirement of further training. Compared with a single-stage model, the two-stage model also shows improved performance.

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Section: Introductionmentioning

confidence: 99%

A Generalised Intelligent Bearing Fault Diagnosis Model Based on a Two-Stage Approach

Kiakojouri,

Lu,

Mirring

et al. 2024

Machines

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“…To prevent operational malfunctions that could potentially result in catastrophic failures, various condition monitoring techniques have been developed for the purpose of fault detection and diagnosis in bearings. Abdelrhman et al [21] introduced a diagnosis and detection model for bearing faults in rotating machinery. Their approach involves the utilization of multivariate analysis of variance to extract parameters from acquired data sets.…”

Section: Introductionmentioning

confidence: 99%

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal

Moshrefi,

Tawfik,

Elsayed

et al. 2024

Sensors

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Ultrasonic diagnostics is the earliest way to predict industrial faults. Usually, a contact microphone is employed for detection, but the recording will be contaminated with noise. In this paper, a dataset that contains 10 main faults of pipelines and motors is analyzed from which 30 different features in the time and frequency domains are extracted. Afterward, for dimensionality reduction, principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are performed. In the subsequent phase, recursive feature elimination (RFE) is employed as a strategic method to analyze and select the most relevant features for the classifiers. Next, predictive models consisting of k-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM) are employed. Then, in order to solve the classification problem, a stacking classifier based on a meta-classifier which combines multiple classification models is introduced. Furthermore, the k-fold cross-validation technique is employed to assess the effectiveness of the model in handling new data for the evaluation of experimental results in ultrasonic fault detection. With the proposed method, the accuracy is around 5% higher over five cross folds with the least amount of variation. The timing evaluation of the meta model on the 64 MHz Cortex M4 microcontroller unit (MCU) revealed an execution time of 11 ms, indicating it could be a promising solution for real-time monitoring.

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“…By employing suitable features extracted from signals, a ML method, such as artificial neural networks (ANNs), multi-nomial logistic regression (MLR), and support vector machines (SVMs) [3], can be used for fault diagnosis by conducting a pattern recognition task. The effectiveness of bearing fault diagnosis is found to be heavily influenced by the quality of features extracted from bearing vibration signals [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A generalised machine learning model based on multinomial logistic regression and frequency features for rolling bearing fault classification

Kiakojouri

Mirring

et al. 2022

insight

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Intelligent fault classification of rolling element bearings (REBs) using machine learning (ML) techniques increases the reliability of industrial assets. One of the main issues associated with ML model development is the lack of training data and, most importantly, the ability of models to be used for applications without specific training data, ie the generalisation capability of models. This study investigates the feasibility of using multinomial logistic regression (MLR) as generalised ML models for rolling element bearing fault classification without the requirement of training data for new bearing designs and varied machine operations. This has been achieved by using bearing characteristic frequencies (BCFs) as inputs to the MLR models extracted by a newly developed hybrid method. The new method combines cepstrum pre-whitening (CPW) and full-band enveloping, which can effectively identify the BCFs in vibration data from various machines. This paper presents the methods of the feature extraction and the development of generalised ML models for REBs based on data from the EU Clean Sky 2 I2BS project1. This model is then validated by data from Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT), available in the public domain without further training.

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Diagnosis model for bearing faults in rotating machinery by using vibration signals and binary logistic regression

Cited by 5 publications

References 7 publications

A Generalised Intelligent Bearing Fault Diagnosis Model Based on a Two-Stage Approach

A Generalised Intelligent Bearing Fault Diagnosis Model Based on a Two-Stage Approach

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal

A generalised machine learning model based on multinomial logistic regression and frequency features for rolling bearing fault classification

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