Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Khan, Sheraz Ali; Kim, Jong‐Myon

doi:10.1155/2016/8729572

Cited by 48 publications

(32 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, before leaving the factory, a high-speed EMU is equipped with many other sensors, such as sensors to monitor the axle box A fault early warning is a part of condition monitoring and different methods are used for the diagnosis of rotary machines, such as bearing, gears, and motors. According to data sources, they can be broadly classified as vibration-based data [1][2][3][4][5][6], acoustic-based data [7,8], temperature-based data [9,10], and fusion-based data [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Fault Early Warning Method for a High-Speed EMU Axle Box Bearing

Liu¹,

Song²,

Geng³

et al. 2020

Sensors

View full text Add to dashboard Cite

An axle box bearing is one of the most important components of high-speed EMUs (electric multiple units), which runs at a very fast speed, suffers a heavy load, and operates under various complex working conditions. Once a bearing fault occurs, it not only has an enormous impact on the railway system, but also poses a threat to personal safety. Therefore, there is significant value in studying a real-time fault early warning of a high-speed EMU axle box bearing. However, to our best knowledge, there are three obvious defects in the existing fault early warning methods used for high-speed EMU axle box bearings: (1) these methods based on vibration are extremely mature, but there are no vibration sensors installed in high-speed EMU axle box because it will greatly increase the manufacturing cost; (2) a TADS (trackside acoustic device system) can effectively detect early failures, but only a portion of railways are equipped with such a facility; and (3) an EMU-ODS (electric multiple unit onboard detection system) has reported numerous untimely warnings, along with warnings of frequent occurrence being missed. Whereupon, a method is proposed to realize the fault early warning of an axle box bearing without installing a vibration sensor on the high-speed EMU in service, namely a MLSTM-iForest (multilayer long short-term memory–isolation forest). First, the time-series data of the temperature-related variables of the axle box bearing is used as the input of MLSTM to predict the axle box bearing temperature in the future. Then, the deviation index of the predicted axle box bearing temperature is calculated. Finally, the deviation index is input into an iForest algorithm for unsupervised classification to realize the fault early warning of an axle box bearing. Experimental results on high-speed EMU operation data sets demonstrated the availability and feasibility of the presented method toward achieving early fault warnings of a high-speed EMU axle box bearing.

show abstract

Section: Introductionmentioning

confidence: 99%

A Real-Time Fault Early Warning Method for a High-Speed EMU Axle Box Bearing

Liu¹,

Song²,

Geng³

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In electromechanical engineering, motion is usually determined by mechanical device structures (e.g., rotating machines or induction motors), which leads to satisfactory records of nearly 70% of the gross energy ingestion in modern manufacturing economics [1,2]. Rotating machineries with induction motors use bearings to moderate friction, which preserves otherwise wasted energy and increases the useful life of a machine.…”

Section: Introductionmentioning

confidence: 99%

“…Bearings play an important role in condition monitoring because, in more than 50% of the cases involving induction motor failures, they are the root cause of the failure [2,8]. Health state diagnosis under variable operating conditions can be employed to improve smooth operability of the machines.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis under Variable Rotational Speeds Using Stockwell Transform-Based Vibration Imaging and Transfer Learning

Hasan

Kim

2018

Applied Sciences

Self Cite

View full text Add to dashboard Cite

In this paper, discrete orthonormal Stockwell transform (DOST)-based vibration imaging is proposed as a preprocessing step for supporting load and rotational speed invariant scenarios for signals of various health conditions. For any health condition, features can easily be extracted from its generated health pattern. To automate the feature selection process, a convolutional neural network (CNN)-based transfer learning (TL) approach for diagnosis has also been introduced. Transfer learning allows an established model to use feature knowledge obtained under one set of working conditions through hidden layers to diagnose faults that occur under other working conditions. The network learns from the massive source dataset, and that knowledge is applied to the target data to identify faults. Using the bearing dataset of Case Western Reserve University, the proposed approach yields an average 99.8% classification accuracy and, specifically, 99.99% for healthy condition (HC), 99.95% for inner race fault (IRF), 99.96% for ball fault (BF), 99.68% for outer race fault for 12 o’clock sensor position (ORF@12), 99.93% for outer race fault for 3 o’clock sensor position (ORF@3), and 99.89% for outer race fault for 6 o’clock sensor position (ORF@6). In this paper, the proposed approach is compared with conventional artificial neural networks (ANNs), support vector machines (SVMs), hierarchical CNNs, and deep autoencoders. The proposed approach outperforms these conventional methods in the accuracy under all working conditions.

show abstract

“…Literature [4] proposed qualitative fault tree analysis. The vibration acceleration and current signal analysis used in [5][6][7] determine the fault by diagnosing the abnormal signal. The hybrid fault decoupling method based on bounded component analysis proposed in [8] is quite effective and representative for solving the complexity problem.…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model

Guo

Yang

2018

Energies

View full text Add to dashboard Cite

Wind turbine condition-monitoring and fault diagnosis have important practical value for wind farms to reduce maintenance cost and improve operating level. Due to the special distribution law of the operating parameters of similar turbines, this paper compares the instantaneous operation parameters of four 1.5 MW turbines with strong correlation of a wind farm. The temperature-power distribution of the gearbox bearings is analyzed to find out the main trend of the turbines and the deviations of individual turbine parameters. At the same time, for the huge amount of data caused by the increase of turbines number and monitoring parameters, this paper uses the huge neural network and multi-hidden layer of a convolutional neural network to model historical data. Finally, the rapid warning and judgment of gearbox bearing over-temperature faults proves that the monitoring method is of great significance for large-scale wind farms.

show abstract

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Cited by 48 publications

References 41 publications

A Real-Time Fault Early Warning Method for a High-Speed EMU Axle Box Bearing

A Real-Time Fault Early Warning Method for a High-Speed EMU Axle Box Bearing

Bearing Fault Diagnosis under Variable Rotational Speeds Using Stockwell Transform-Based Vibration Imaging and Transfer Learning

Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model

Contact Info

Product

Resources

About