Efficient gear fault feature selection based on moth-flame optimisation in discrete wavelet packet analysis domain

Abstract

Rotating machinery-a crucial component in modern industry, requires vigilant monitoring such that any potential malfunction of its electromechanical systems can be detected prior to a fatal breakdown. However, identifying faulty signals from a defective rotating machinery is challenging due to complex dynamical behaviour. Therefore, the search for features which best describe the characteristic of different fault conditions is often crucial for condition monitoring of rotating machinery. For this purpose, this study used the intensification and diversification properties of the recently proposed moth-flame optimisation (MFO) algorithm and utilised the algorithm in the proposed feature selection scheme. The proposed method consisted of three parts. First, the vibration signals of gear with different fault conditions were decomposed by a fourth-level discrete wavelet packet transform, and the statistical features at all constructed nodes were derived. Second, the MFO algorithm was utilised to select the optimal discriminative features. Lastly, the MFO-selected features were used as the input for a support vector machine (SVM) diagnostic model to identify fault patterns. To further demonstrate the superiority of the proposed method, other feature selection approaches were applied, including randomly selected features and complete features, and other diagnostic models, namely the multilayer perceptron neural network and k-nearest neighbour. Comparative experiments demonstrated that SVM with the MFO-selected features outperformed the others, with the classification accuracy of 99.60%, thus validating its effectiveness.

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

“…12–17 The importance of CM and FD engineering processes comes from the serious need for continuous monitoring of the health of the industrial components and systems through their life. 18–23 Moreover, the main goals are to improve the reliability, 24–26 safety, 27–29 availability, 30,31 efficiency, 32,33 and to reduce the maintenance costs 34,35 as well as to avoid a breakdown or sudden failures. 36,37 In modern industrial applications, 38,39 rotating machinery (RM) 40–42 becomes the most important equipment.…”

Building a High Accuracy Transfer Learning-Based Quality Inspection System at Low Costs

“…Recent progressions that are concerned with the inspection of gears have broadly utilized mathematical analysis strategies to achieve inspection tasks, for example, detection of plastic gear defects with image processing [2], using wavelet transform for fault detection of planetary gears system [3], detection of gear faults using: morlet-wavelet filter [4], adaptive wavelet threshold de-noising [5] and cosine similarity, wavelet transform and Hilbert transform [6]. Moreover, gear faults diagnosis using: adaptive impulsive wavelet transform [7], utilizing extreme learning machines and numerical simulation [8], discrete wavelet packet for feature selection of gear faults [9] and inspection of polymer spur gears [10]. Advanced technologies like AI and CV are also employed for inspection, such as: using machine vision for spur gears parameters measurement [11], using CV to detect gear tooth number [12], using artificial vision for quality control of spur gears [13], inspection of gear faults using support vector machines (SVMs) and artificial neural networks (ANNs) [14], determining fine-pitch gears centers using machine vision [15], gear faults with convolutional neural networks (CNNs) [16], gears diagnosis using CNNs [17] and inspection of plastic gears using ANN and SVM based method [18].…”