scholarly journals Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach

2021 ◽  
Vol 11 (13) ◽  
pp. 5792
Author(s):  
Siu Ki Ho ◽  
Harish Chandra Nedunuri ◽  
Wamadeva Balachandran ◽  
Jamil Kanfoud ◽  
Tat-Hean Gan
Keyword(s):  
Feature Extraction ◽  
Random Vibration ◽  
Correct Diagnosis ◽  
Ground Truth ◽  
False Alarms ◽  
Optimal Filtering ◽  
Vibration Data ◽  
Reliable Performance ◽  
Vibration Signatures ◽  
Industrial Machine

Machinery with several rotating and stationary components tends to produce non-stationary and random vibration signatures due to the fluctuations in the input loads and process defects due to long hours of operation. Traditional heuristics methods are suitable for the detection of fault signatures, however, they become more complicated when the level of uncertainty or randomness exceeds beyond control. A novel methodology to identify these fault signatures using optimal filtering of vibration data is proposed to eliminate any false alarms and is expected to provide a higher probability of correct diagnosis. In this paper, a detailed pipeline of the algorithms are presented along with the results of the investigation that was carried out. These investigations are performed using open-source vibration data published by the NASA prognostics centre. The performance of these algorithms are evaluated based on the ground truth results published by NASA researchers. Based on the performance of these algorithms several parameters are fine-tuned to ensure generalisation and reliable performance.

Analysis of Triaxial Vibration Data for Health Monitoring of Helicopter Gearboxes

2003 ◽  
Vol 125 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Irem Y. Tumer ◽  
Edward M. Huff
Keyword(s):  
Vibration Monitoring ◽  
False Alarms ◽  
Frequency Content ◽  
Sources Of Information ◽  
Additional Information ◽  
Vibration Data ◽  
Statistical Variation ◽  
Monitoring And Diagnostics ◽  
Helicopter Transmission ◽  
Vibration Signatures

Research on the nature of the vibration data collected from helicopter transmissions during flight experiments has led to several crucial observations believed to be responsible for the high rates of false alarms and missed detections in aircraft vibration monitoring systems. This work focuses on one such finding, namely, the need to consider additional sources of information about system vibrations. In this light, helicopter transmission vibration data, collected using triaxial accelerometers, are explored in three different directions, analyzed for content, and then combined using Principal Components Analysis (PCA) to analyze changes in directionality. The frequency content of the three different directions is compared and analyzed using time-synchronously averaged vibration data. To provide a method for analysis and monitoring purposes, the triaxial data are decorrelated using a mathematical transformation, and compared to the original axes to determine their differences. The benefits of using triaxial data for vibration monitoring and diagnostics are explored by analyzing the changes in the direction of the principal axis of vibration formed using all three axes of vibration. The statistical variation introduced due to the experimental variables is further analyzed using an Analysis of Variance approach to determine the effect of each variable on the overall signature. The results indicate that triaxial accelerometers can provide additional information about the frequency content of helicopter gearbox vibrations, and provide researchers and industry with a novel method of capturing and monitoring triaxial changes in the baseline vibration signatures.

scholarly journals Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning

2020 ◽  
Vol 10 (21) ◽  
pp. 7413
Author(s):  
Taing Borith ◽  
Sadirbaev Bakhit ◽  
Aziz Nasridinov ◽  
Kwan-Hee Yoo
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Active State ◽  
Economic Losses ◽  
Inactive State ◽  
Machine Learning Methods ◽  
Catastrophic Failures ◽  
Modern Manufacturing ◽  
Active Detection ◽  
Industrial Machine

In modern manufacturing, the detection and prediction of machine anomalies, i.e., the inactive state of the machine during operation, is an important issue. Accurate inactive state detection models for factory machines can result in increased productivity. Moreover, they can guide engineers in implementing appropriate maintenance actions, which can prevent catastrophic failures and minimize economic losses. In this paper, we present a novel two-step data-driven method for the non-active detection of industry machines. First, we propose a feature extraction approach that aims to better distinguish the pattern of the active state and non-active state of the machine by multiple statistical analyses, such as reliability, time-domain, and frequency-domain analyses. Next, we construct a method to detect the active and non-active status of an industrial machine by applying various machine learning methods. The performance evaluation with a real-world dataset from the automobile part manufacturer demonstrates the proposed method achieves high accuracy.

scholarly journals Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Qingbo He ◽  
Xiaoxi Ding ◽  
Yuanyuan Pan
Keyword(s):  
Feature Extraction ◽  
Wavelet Packet ◽  
Fault Classification ◽  
Feature Extraction Method ◽  
Locality Preserving Projections ◽  
Vibration Data ◽  
Pattern Structure ◽  
Locality Preserving ◽  
Machine Fault

Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities.

Using Triaxial Accelerometer Data for Vibration Monitoring of Helicopter Gearboxes

2001 ◽  
Author(s):  
Irem Y. Tumer ◽  
Edward M. Huff
Keyword(s):  
Vibration Monitoring ◽  
Monitoring Systems ◽  
Accelerometer Data ◽  
Frequency Content ◽  
Additional Information ◽  
Vibration Data ◽  
Statistical Variation ◽  
Novel Method ◽  
Monitoring And Diagnostics ◽  
Vibration Signatures

Abstract Typical vibration monitoring systems for helicopter gearboxes rely on single-axis accelerometer data. This paper investigates whether triaxial accelerometers can provide crucial flight regime information for helicopter gearbox monitoring systems. The frequency content of the three different directions is compared and analyzed using time-synchronously averaged vibration data. The triaxial data are decorrelated using a mathematical transformation, and compared to the original axes to determine their optimality. The benefits of using triaxial data for vibration monitoring and diagnostics are explored by analyzing the changes in the direction of the principal axis of vibration formed using all three axes of vibration. The statistical variation introduced due to the experimental variables is further analyzed using an Analysis of Variance approach to determine the effect of each variable on the overall signature. The results indicate that triaxial accelerometers can provide additional information about the frequency content of helicopter gearbox vibrations, providing researchers and industry with a novel method of capturing and monitoring changes in the baseline vibration signatures.

scholarly journals A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7155
Author(s):  
Zejun Zheng ◽  
Dongli Song ◽  
Xiao Xu ◽  
Lei Lei
Keyword(s):  
Feature Extraction ◽  
Health Status ◽  
Fault Diagnosis ◽  
High Speed ◽  
Energy Operator ◽  
Ar Model ◽  
Diagnostic Efficiency ◽  
Heavy Load ◽  
Feature Extraction Method ◽  
Vibration Data

The axle box bearing of bogie is one of the key components of the rail transit train, which can ensure the rotary motion of wheelsets and make the wheelsets adapt to the conditions of uneven railways. At the same time, the axle box bearing also exposes most of the load of the car body. Long-time high-speed rotation and heavy load make the axle box bearing prone to failure. If the bearing failure occurs, it will greatly affect the safety of the train. Therefore, it is extremely important to monitor the health status of the axle box bearing. At present, the health status of the axle box bearing is mainly monitored by vibration information and temperature information. Compared with the temperature data, the vibration data can more easily detect the early fault of the bearing, and early warning of the bearing state can avoid the occurrence of serious fault in time. Therefore, this paper is based on the vibration data of the axle box bearing to carry out adaptive fault diagnosis of bearing. First, the AR model predictive filter is used to denoise the vibration signal of the bearing, and then the signal is whitened in the frequency domain. Finally, the characteristic value of vibration data is extracted by energy operator demodulation, and the fault type is determined by comparing with the theoretical value. Through the analysis of the constructed simulation signal data, the characteristic parameters of the data can be effectively extracted. The experimental data collected from the bearing testbed of high-speed train are analyzed and verified, which further proves the effectiveness of the feature extraction method proposed in this paper. Compared with other axle box bearing fault diagnosis methods, the innovation of the proposed method is that the signal is denoised twice by using AR filter and spectrum whitening, and the adaptive extraction of fault features is realized by using energy operator. At the same time, the steps of setting parameters in the process of feature extraction are avoided in other feature extraction methods, which improves the diagnostic efficiency and is conducive to use in online monitoring system.

Feature extraction of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy

2020 ◽  
pp. 107754632092566 ◽  
Author(s):  
HongChao Wang ◽  
WenLiao Du
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Sparse Decomposition ◽  
Vibration Data ◽  
Fault Features ◽  
Band Pass

As the key rotating parts in machinery, it is crucial to extract the latent fault features of rolling bearing in machinery condition monitoring to avoid the occurrence of sudden accidents. Unfortunately, the latent fault features are hard to extract by using the traditional signal processing method such as envelope demodulation because the effect of envelope demodulation is influenced strongly by the degree of background noise. Sparse decomposition, as a new promising method being able of capturing the latent fault feature components buried in the vibration signal, has attracted a lot of attentions, especially the predefined dictionary-based sparse decomposition methods. However, the feature extraction effect of the predefined dictionary-based sparse decomposition depends on whether the prior knowledge of the analyzed signal is sufficient or not. To overcome the above problems, a feature extraction method of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy is proposed in the article. First, a self-learned sparse atomics method is applied on the early weak vibration signal of rolling bearing and several self-learned atomics are obtained. Then, the self-learned atomics owing bigger kurtosis values are selected and used to reconstruct the vibration signal to remove the other interference signals. Subsequently, the frequency band entropy method is used to analyze the reconstructed vibration signal, and the optimal parameter of band-pass filter could be calculated. At last, the reconstructed vibration signal is filtered using the optimal band-pass filter, envelope demodulation on the filtered signal is applied, and better fault feature is extracted. The feasibility and effectiveness of the proposed method are verified through the vibration data of the accelerated fatigue life test of rolling bearing. Besides, the analysis results of the same vibration data using Autogram and spectral kurtosis methods are also presented to highlight the superiority of the proposed method.

Inductive Estimation Arithmetic for Random Vibration Data

2011 ◽  
Vol 219-220 ◽  
pp. 1113-1116
Author(s):  
Yong Jie Zhang ◽  
Xiao Hu Zhang ◽  
Bin Tuan Wang
Keyword(s):  
Data Processing ◽  
Numerical Experiments ◽  
Random Vibration ◽  
Measured Data ◽  
Reliability Test ◽  
Vibration Data ◽  
Inductive Methods

The vibration data processing of environmental spectrum is an important part of environmental reliability test for the aircraft. Based on the inductive methods for environmental measured data of vibration, an inductive arithmetic is employed to estimate the random vibration data of aircraft surface in Matlab system. Numerical experiments show that the inductive estimation arithmetic is effective, reliable and applicable for the environmental measured data of random vibration.

Sign in / Sign up

Export Citation Format

Share Document