Rolling bearing fault diagnosis via STFT and improved instantaneous frequency estimation method

The accuracy of bearing fault diagnosis is of great significance for the reliable operation of rotating machinery. In recent years, increasing attention has been paid to intelligent fault diagnosis techniques based on deep learning. However, most of these methods are based on supervised learning with a large amount of labeled data, which is a challenge for industrial applications. To reduce the dependence on labeled data, a self-supervised joint learning (SSJL) fault diagnosis method based on three-channel vibration images is proposed. The method combines self-supervised learning with supervised learning, makes full use of unlabeled data to learn fault features, and further improves the feature recognition rate by transforming the data into three-channel vibration images. The validity of the method was verified using two typical data sets from a motor bearing. Experimental results show that this method has higher diagnostic accuracy for small quantities of labeled data and is superior to the existing methods.

Section: Proposed Methodsmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images

Zhang

Chen

Kong

2021

“…STFT uses a fixed window function and is usually used to analyze piecewise stationary signals or quasi-stationary signals. However, the frequency and time resolution cannot be taken into account in the meantime [ 48 ]. DWT is a discretization to the scale and translation of basic wavelet and generally refers to two-scale wavelet transform.…”

Section: Proposed Intelligent Fault Diagnosis Methodsmentioning

confidence: 99%

An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

Tang

Yuan

Zhu

et al. 2020

A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.

“…The problem of diagnosing drive transmission systems, including those equipped with toothed gears, is a difficult scientific topic [ 11 ], especially when diagnostics are carried out on the basis of vibration signals. For this reason, the recorded signals are subjected by scientists to many types of analyses; e.g., in the time domain (synchronous averaging [ 12 ] or signal resampling [ 13 ]), in the frequency domain [ 14 ], and in the time–frequency domain [ 12 , 15 , 16 ], including the very often used short-time Fourier transform (STFT) [ 13 , 15 , 17 , 18 , 19 , 20 , 21 ]. In addition, signal analysis methods using artificial neural networks and genetic algorithms are used [ 16 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Data Interpretation of Vibration Signals Registered in Different Locations for System Condition Monitoring of a Three-Stage Gear Transmission Operating under Difficult Conditions

Wojnar

Burdzik

Wieczorek

et al. 2021

This article provides a discussion of the results of studies on the original system condition monitoring of a three-stage transmission with a bevel–cylindrical–planetary configuration installed in an experimental scraper conveyor. Due to the high vibroactivity of gear transmissions operating under the impact of a scraper conveyor’s chain drive, these unwanted effects of machine operating vibrations were assumed to be applied. For purposes of the study, vibrations were measured on the driving transmission housing in an idling scraper conveyor. The main purpose of the study was to establish the frequencies characteristic of the gear transmission, and to determine whether it was possible to run vibroacoustic diagnostics of the same transmission under conditions with a considerable impact of the conveyor chain. An additional cognitively significant research goal was the analysis of the dependence of the diagnostic utility of the signal depending on the sensor mounting point. Five different locations of three-axis sensors oriented to the next stages and various types of gears were determined, as well as places characterized by high spatial accessibility, which are often selected as places for measuring the vibration of gears. Using MATLAB software, a program was written that was calibrated and adapted to the specifics of the measuring equipment based on the collected test results. As a result, it was possible to obtain a multidimensional data interpretation of vibration signals of system condition monitoring of a three-stage gear transmission operating under difficult conditions. The results were based on signals registered on the real three-stage gear transmission operating under the impact of a scraper conveyor’s chain drive.