scholarly journals Development of Indicator of Data Sufficiency for Feature-based Early Time Series Classification with Applications of Bearing Fault Diagnosis

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 790
Author(s):  
Gilseung Ahn ◽  
Hwanchul Lee ◽  
Jisu Park ◽  
Sun Hur
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Early Time ◽  
Time Series Classification ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Benchmark Datasets ◽  
Feature Based ◽  
General Time

Diagnosis of bearing faults is crucial in various industries. Time series classification (TSC) assigns each time series to one of a set of pre-defined classes, such as normal and fault, and has been regarded as an appropriate approach for bearing fault diagnosis. Considering late and inaccurate fault diagnosis may have a significant impact on maintenance costs, it is important to classify bearing signals as early and accurately as possible. TSC, however, has a major limitation, which is that a time series cannot be classified until the entire series is collected, implying that a fault cannot be diagnosed using TSC in advance. Therefore, it is important to classify a partially collected time series for early time series classification (ESTC), which is a TSC that considers both accuracy and earliness. Feature-based TSCs can handle this, but the problem is to determine whether a partially collected time series is enough for a decision that is still unsolved. Motivated by this, we propose an indicator of data sufficiency to determine whether a feature-based fault detection classifier can start classifying partially collected signals in order to diagnose bearing faults as early and accurately as possible. The indicator is trained based on the cosine similarity between signals that were collected fully and partially as input to the classifier. In addition, a parameter setting method for efficiently training the indicator is also proposed. The results of experiments using four benchmark datasets verified that the proposed indicator increased both accuracy and earliness compared with the previous time series classification method and general time series classification.

Difference Histograms: A new tool for time series analysis applied to bearing fault diagnosis

2009 ◽  
Vol 30 (6) ◽  
pp. 595-599 ◽  
Author(s):  
Barend J. van Wyk ◽  
Michaël A. van Wyk ◽  
Guoyuan Qi
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Time Series Analysis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Series Analysis

scholarly journals Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6886
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Embedded Systems ◽  
Image Size ◽  
Bearing Faults ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Machine Health Monitoring ◽  
System Resources

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.

Dynamic linear models-based time series decomposition and its application on bearing fault diagnosis

2013 ◽  
Vol 21 (5) ◽  
pp. 975-988 ◽  
Author(s):  
Haifeng Tang ◽  
Jin Chen ◽  
Guangming Dong
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Linear Models ◽  
Dynamic Linear Models ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Time Series Decomposition

A heterogeneous fault diagnosis method for bearings in gearbox

2014 ◽  
Vol 229 (8) ◽  
pp. 1491-1499 ◽  
Author(s):  
Xiaohui Chen ◽  
Lei Xiao ◽  
Xinghui Zhang ◽  
Zhenxiang Liu
Keyword(s):  
Fault Diagnosis ◽  
Bearing Failure ◽  
Optimum Frequency ◽  
Bearing Faults ◽  
Outer Race ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Inner Race

Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains two bearing fault modes, inner race fault and outer race fault. The results demonstrate that the proposed method can detect the bearing fault easier than spectral Kurtosis and envelope Kurtosis.

scholarly journals Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1510
Author(s):  
Mostafa Rostaghi ◽  
Mohammad Mahdi Khatibi ◽  
Mohammad Reza Ashory ◽  
Hamed Azami
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Inference ◽  
Fault Classification ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Signal Complexity ◽  
Inference Systems ◽  
Measuring Signal ◽  
Multiscale Algorithms

Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults. Numerous studies have investigated multiscale algorithms; nevertheless, multiscale algorithms using the first moment lose important complexity data. Accordingly, generalized multiscale algorithms have been recently introduced. The present research examined the use of refined composite generalized multiscale dispersion entropy (RCGMDispEn) based on the second moment (variance) and third moment (skewness) along with refined composite multiscale dispersion entropy (RCMDispEn) in bearing fault diagnosis. Moreover, multiclass FCM-ANFIS, which is a combination of adaptive network-based fuzzy inference systems (ANFIS), was developed to improve the efficiency of rotating machinery fault classification. According to the results, it is recommended that generalized multiscale algorithms based on variance and skewness be examined for diagnosis, along with multiscale algorithms, and be used to achieve an improvement in the results. The simultaneous usage of the multiscale algorithm and generalized multiscale algorithms improved the results in all three real datasets used in this study.

scholarly journals Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1884 ◽  
Author(s):  
Rafia Nishat Toma ◽  
Alexander E. Prosvirin ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Statistical Features ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Motor Current ◽  
Comparison Results

Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.

Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults

2017 ◽  
Vol 17 (2) ◽  
pp. 156-168 ◽  
Author(s):  
Cheng-Wei Fei ◽  
Yat-Sze Choy ◽  
Guang-Chen Bai ◽  
Wen-Zhong Tang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Rolling Bearing ◽  
Distance Method ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Entropy Distance

To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.

scholarly journals Bearing Fault Diagnosis for Direct-Drive Wind Turbines via Current-Demodulated Signals

2013 ◽  
Vol 60 (8) ◽  
pp. 3419-3428 ◽  
Author(s):  
Xiang Gong ◽  
Wei Qiao
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Economic Benefits ◽  
Direct Drive ◽  
Current Frequency ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Stator Current

Bearing faults account for a large portion of all faults in wind turbine generators (WTGs). Current-based bearing fault diagnosis techniques have great economic benefits and are potential to be adopted by the wind energy industry. This paper models the modulation effects of bearing faults on the stator currents of a direct-drive wind turbine equipped with a permanent-magnet synchronous generator (PMSG) operating with a variable shaft rotating frequency. Based on the analysis, a method consisting of appropriate current frequency and amplitude demodulation algorithms and a 1P-invariant power spectrum density algorithm is proposed for bearing fault diagnosis of variable-speed direct-drive wind turbines using only one-phase stator current measurements, where 1P frequency stands for the shaft rotating frequency of a wind turbine. Experimental results on a direct-drive wind turbine equipped with a PMSG operating in a wind tunnel are provided to verify the proposed fault diagnosis method. The proposed method is demonstrated to have advantages over the method of directly using stator current measurements for WTG bearing fault diagnosis.

Sensitivity Analysis and Experimental Research on Ball Bearing Early Fault Diagnosis Based on Testing Signal From Casing

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
G. Chen ◽  
T. F. Hao ◽  
H. F. Wang ◽  
B. Zhao ◽  
J. Wang ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Fault Diagnosis ◽  
Ball Bearing ◽  
Ball Bearings ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Vibration Attenuation ◽  
Aero Engine

The ball bearings of an aero-engine are key parts that frequently fail, and it is very important to effectively carry out fault diagnosis of the ball bearings. However, in the present research work, the ball bearing faults characteristics are extracted mainly from the bearing house signals, it is well known that usually only the casing signals can be measured in practical aero-engine test, and the ball bearing faults characteristics will greatly weaken after transmitting to the casing from the bearing house, therefore, it is very important to extract the fault characteristics of ball bearings from casing vibration signals for the ball bearing fault diagnosis in the practical aero-engine. In this study, simulation experiments for ball bearing faults are conducted using two rotor experimental rigs with casings. In addition, by means of the impulse response method, the transfer characteristics from the ball bearings to casing measuring points are measured, and a sensitivity analysis is performed. Faults are created on the inner ring, outer ring, and ball of the ball bearings in the two experimental rigs. The ball bearing experiments are carried out, and the fault features are extracted by means of a wavelet envelope analysis. The experimental results indicate that, with high connection stiffness between the bearing house and the casing, there is little vibration attenuation. However, with low connection stiffness, the vibration attenuation is great. After the impulse vibrations caused by the ball bearing faults are transmitted to the casing, the casing vibration is very weak and is often submerged in other signals. However, the ball bearing fault characteristic frequencies can still be effectively extracted from the weak casing vibration signals by using a wavelet envelope analysis. The research results in this study provide an experimental basis for a ball bearing fault diagnosis based on a casing test signal from a practical aero-engine.

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