A fast filtering method based on adaptive impulsive wavelet for the gear fault diagnosis

Abstract

The useful fault features applied for the fault diagnosis are usually overwhelmed by noise and other interference factors in rotation machinery. The impulses masked in vibration signals can represent the faults of gears or bearings in a gearbox. The key to finding impulsive components is to identify the modeling parameters (such as damping ratio, central frequency) of a transient (Morlet wavelet, Laplace wavelet), which can be used as an adaptive filter to denoise the vibration signal. However, its engineering application is limited by the time-consuming computation. In order to tackle this issue, a fast algorithm based on an adaptive impulsive wavelet is proposed to filter the fault signal so that the fault characteristic frequency can be identified. Firstly, a correlation coefficient maximum criterion is employed to find one of the optimal parameters of the impulsive wavelet. Then, the other parameter is optimized by the minimum Shannon wavelet entropy criterion. Finally, the impulsive wavelet filter with optimal parameters is applied to extract the fault characteristic frequency. Simulation signals are applied to verify the efficiency of the proposed approach, and comparison analysis is conducted as well. Further, the proposed method is applied to detect the gear fault of a gearbox. The experimental results show that the proposed method is effective with high efficiency.

Transformer winding mechanical fault diagnosis method based on closing transient acoustic vibration signal

“…i is the primary winding current [5]. The average inductance of av L during steady-state operation is used to simulate the core excitation inductance of a single-phase transformer, and the above differential equation is solved by substituting the initial remanence of the transformer at the time of…”

Editorial

“…This special issue aims to provide a platform for researchers and practitioners to showcase new research findings, application cases and explore emerging technologies in the implementation of intelligent manufacturing. There are 14 original research papers collected in this issue, covering industrial digital technologies, 1,2 sustainable intelligent manufacturing technologies, [3][4][5] design for intelligent manufacturing, 6 intelligent monitoring technologies for smart workshop and machinery, 7,8 intelligent machining, 9 assembly/disassembly [10][11][12][13] and measuring technologies. 14 The distributed artificial intelligence system has been considered as an important approach to meet the everincreasing demand for manufacturing personalised products.…”

“…The approach can be used in intelligent workshop behaviour monitoring systems to effectively realise the safety management of workshop personnel. To overcome the challenges of high level of noise and time-consuming computation in gear fault diagnosis in rotation machinery, Yu et al 9 proposed a fast algorithm based on an adaptive impulsive wavelet to filter the fault signal so that the fault characteristic frequency can be identified. High efficiency was achieved when applying the proposed method to detect the gear fault of a gearbox.…”

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].…”