Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components

Abstract

Ball bearings are one of the most common components used in rotating machines. They reduce the rotational friction between the shaft and fixed components and maintain the center line of rotation of the shaft. A damaged bearing will cause abnormal vibration and noise, and often results in machine failure and loss of production. In this study the public database on ball bearings, provided by the Vibration Institute of Machinery Failure Prevention Technology (MFPT), was used for data retrieval and analysis, and a diagnosis model was created according to the data sets of the bearing in the database. Three different approaches were used for the extraction of features and a classifier was used to implement a diagnostic system. The aim of this study was a comparison of three approaches. The first was the Short-time Fourier Transform (STFT) where the time-frequency domain image is extracted as the feature used for status identification. The second and third approaches were based on the Chen-Lee Chaotic and the Lorenz Chaotic Systems and chaotic dynamic error maps were used in analysis and feature status identification. Chaotic systems are particularly sensitive to the slightest changes in input signals, and the time domain signals from bearings in different conditions were mapped onto individual images. The feature images extracted by the three different approaches were then used for training and verification in a Convolutional Neural Network (CNN). From the results of the experiments, it can be seen that all three approaches gave high identification rates. The interactive verification identification rate of the Chen-Lee chaotic system with CNN under three statuses reached 98.33%, and it also had the best computational efficiency in the condition without losing any classification accuracy. This will make a substantial contribution to real-time ball bearing fault diagnosis.

Vibration Image Representations for Fault Diagnosis of Rotating Machines: A Review

“…Figure 18 presents examples of the generated vibration images using STFT from the bearing vibration signals with six health conditions, as described in Section 2.1.1 above. Numerous studies employed the STFT-based images as inputs into a classifier to perform the fault diagnosis task [82][83][84][85][86][87][88], while other studies have attempted to produce features based on the STFT images of the original vibration signals for fault diagnosis. The following subsections highlight two of these techniques.…”

“…Numerous studies employed the STFT-based images as inputs into a classifier to perform the fault diagnosis task [82][83][84][85][86][87][88], while other studies have attempted to produce features based on the STFT images of the original vibration signals for fault diagnosis. The following subsections highlight two of these techniques.…”

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

“…The same objective was pursued in [14] by means of the marginal time integration of STFTs. Bearing fault classification by means of non-negative matrix factorization or convolutional neural networks and STFT was evaluated in [15,16].…”

Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network

“…With the advancement of online monitoring and fault diagnosis technology for rotating machinery, a series of fault diagnosis methods have been introduced to fault diagnosis of rotating machinery in the past half century [ 3 , 4 ]. Traditional fault diagnosis methods can be mainly divided into the construction of fault features and the use of pattern recognition methods for fault classification.…”