Vibration Based Gear Fault Diagnosis under Empirical Mode Decomposition and Power Spectrum Density Analysis

Akram, Muhammad; Khushnood, Shahab; Tariq, Syeda; Hafiz, Ali; Nizam, Luqman Ahmad

doi:10.12913/22998624/111663

“…The test model is primarily created for analyzing vibration of gear at various RPM, and then a particular fault was presented in driven gear for different damage conditions. Data recorded by a wireless tri-axial accelerometer was analyzed utilizing PSD and EMD method and result express that EMD is better than traditional time waveform and PSD method [17].…”

Section: Lstm-fcn (Longmentioning

confidence: 99%

Faulty gear diagnosis using weighted PCA with swish activated BLSTM classifier

Ghulanavar

¹

,

Jagadeesh

²

,

Dama

³

2022

Multimed Tools Appl

1

0

View full text Add to dashboard Cite

The early faulty gear diagnosis is most necessary in the industry. In the current decade, with the tremendous growth of ANN (Artificial Neural Network), the researcher planned to use DL (Deep Learning) methods to sketch out faults in gear in an early stage. Traditional gear fault diagnosis method mostly utilizes deep NN (Neural Network) related to tine sequence of gathered signals. In this instance, feature extraction in the direction of inverse time domain signal is commonly ignored. To overcome this issue, here in this paper, proposed Weighted Principal Component Analysis (WPCA) and BLSTM (Bi-Directional Long Short Term Memory) along with Swish Activation function for faulty gear diagnosis from the vibration signals. WPCA is utilized to extract multi-scale features related to faulty gear from the vibration signal. Likewise, BLSTM is used to classify the extracted features to diagnose the fault in an earlier stage. Several experiments were conducted to evaluate the proposed work of categorizing the defects in gear from the vibrating signal. Experiments were conducted on three kinds of the dataset to classify the type of faulty gear accurately. The proposed work proves its superiority in organizing the gear faults in a most efficient way than existing methods.

show abstract

“…The test model is primarily created for analyzing vibration of gear at various RPM, and then a particular fault was presented in driven gear for different damage conditions. Data recorded by a wireless tri-axial accelerometer was analyzed utilizing PSD and EMD method and result express that EMD is better than traditional time waveform and PSD method [17]. Furthermore, the novel fault diagnosis technique that integrates three methods like EMD, PSO-SVM (Particle Swarm Optimization-Support Vector Machine) and fractal box dimension.…”

Section: Related Workmentioning

confidence: 99%

Faulty Gear Diagnosis Using Weighted PCA with Swish Activated BLSTM Classifier

Ghulanavar¹,

Jagadeesh²,

Dama³

2021

Preprint

0

View full text Add to dashboard Cite

The early faulty gear diagnosis is most necessary in the industry. In the current decade, with the tremendous growth of ANN (Artificial Neural Network), the researcher planned to use DL (Deep Learning) methods to sketch out faults in gear in an early stage. Traditional gear fault diagnosis method mostly utilizes deep NN (Neural Network) related to tine sequence of gathered signals. In this instance, feature extraction in the direction of inverse time domain signal is commonly ignored. To overcome this issue, here in this paper, proposed Weighted Principal Component Analysis (WPCA) and BLSTM (Bi-Directional Long Short Term Memory) along with Swish Activation function for faulty gear diagnosis from the vibration signals. WPCA is utilized to extract multi-scale features related to faulty gear from the vibration signal. Likewise, BLSTM is used to classify the extracted features to diagnose the fault in an earlier stage. Several experiments were conducted to evaluate the proposed work of categorizing the defects in gear from the vibrating signal. Experiments were conducted on three kinds of the dataset to classify the type of faulty gear accurately. The proposed work proves its superiority in organizing the gear faults in a most efficient way than existing methods.

show abstract

“…For example, Han et al decomposes nonstationary signals into several IMF by EMD, and selects IMF with sensitive fault characteristic frequency as the input of SVM for gear fault diagnosis [ 14 ]. According to the operating parameters and frequency spectrum, Muhammad et al selected IMF, including gear fault characteristics, and finally realized the fault diagnosis of gearbox gears [ 15 ]. In addition, VMD is widely used.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox

Wang,

Guo,

Zhao

et al. 2023

Sensors

View full text Add to dashboard Cite

This paper introduces a fault diagnosis method for mine scraper conveyor gearbox gears using motor current signature analysis (MCSA). This approach solves problems related to gear fault characteristics that are affected by coal flow load and power frequency, which are difficult to extract efficiently. A fault diagnosis method is proposed based on variational mode decomposition (VMD)–Hilbert spectrum and ShuffleNet-V2. Firstly, the gear current signal is decomposed into a series of intrinsic mode functions (IMF) by using VMD, and the sensitive parameters of VMD are optimized by using a genetic algorithm (GA). The Sensitive IMF algorithm judges the modal function sensitive to fault information after VMD processing. By analyzing the local Hilbert instantaneous energy spectrum for fault-sensitive IMF, an accurate expression of signal energy changing with time is obtained to generate the local Hilbert immediate energy spectrum dataset of different fault gears. Finally, ShuffleNet-V2 is used to identify the gear fault state. The experimental results show that the accuracy of the ShuffleNet-V2 neural network is 91.66% after 778 s.

show abstract

Vibration Based Gear Fault Diagnosis under Empirical Mode Decomposition and Power Spectrum Density Analysis

Cited by 7 publications

References 20 publications

Faulty gear diagnosis using weighted PCA with swish activated BLSTM classifier

Faulty gear diagnosis using weighted PCA with swish activated BLSTM classifier

Faulty Gear Diagnosis Using Weighted PCA with Swish Activated BLSTM Classifier

Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox

Contact Info

Product

Resources

About