Gear pitting fault diagnosis using disentangled features from unsupervised deep learning

2019 ◽  
Vol 233 (5) ◽  
pp. 719-730 ◽  
Author(s):  
Yongzhi Qu ◽  
Yue Zhang ◽  
Miao He ◽  
David He ◽  
Chen Jiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Training Data ◽  
Health State ◽  
Learning Approaches ◽  
Time Frequency ◽  
Fault Diagnosis And Prognosis ◽  
Machinery Fault Diagnosis

Effective feature extraction is critical for machinery fault diagnosis and prognosis. The use of time–frequency features for machinery fault diagnosis has prevailed in the last decade. However, more attentions have been drawn to machine learning–based features. While time–frequency domain features can be directly correlated to fault types and fault levels, data-driven features are typically abstract representations. Therefore, classical machine learning approaches require large amount of training data to classify these abstract features for fault diagnosis. This article proposed a fully unsupervised feature extraction method for “meaningful” feature mining, named disentangled tone mining. It is shown that disentangled tone mining can effectively extract the hidden “trend” associated with machinery health state, which can be used directly for online anomaly detection and prediction. Compared with wavelet transform and time domain statistics, disentangled tone mining can better extract fault-related features and reflect the fault degradation process. Shallow methods, such as principal component analysis, multidimensional scaling and single-layer sparse autoencoder, are shown to be inferior in terms of disentangled feature learning for machinery signals. Simulation analysis is also provided to demonstrate and explain the potential mechanism underlying the proposed method.

scholarly journals Fault Diagnosis via Neural Ordinary Differential Equations

2021 ◽  
Vol 11 (9) ◽  
pp. 3776
Author(s):  
Luis Enciso-Salas ◽  
Gustavo Pérez-Zuñiga ◽  
Javier Sotomayor-Moriano
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Data Augmentation ◽  
Hybrid Approach ◽  
Training Data ◽  
Data Series ◽  
Learning Approaches ◽  
Data Set

Implementation of model-based fault diagnosis systems can be a difficult task due to the complex dynamics of most systems, an appealing alternative to avoiding modeling is to use machine learning-based techniques for which the implementation is more affordable nowadays. However, the latter approach often requires extensive data processing. In this paper, a hybrid approach using recent developments in neural ordinary differential equations is proposed. This approach enables us to combine a natural deep learning technique with an estimated model of the system, making the training simpler and more efficient. For evaluation of this methodology, a nonlinear benchmark system is used by simulation of faults in actuators, sensors, and process. Simulation results show that the proposed methodology requires less processing for the training in comparison with conventional machine learning approaches since the data-set is directly taken from the measurements and inputs. Furthermore, since the model used in the essay is only a structural approximation of the plant; no advanced modeling is required. This approach can also alleviate some pitfalls of training data-series, such as complicated data augmentation methodologies and the necessity for big amounts of data.

scholarly journals Spectral Feature Extraction Using Partial and General Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bin Jiang ◽  
Xi Fang ◽  
Yang Liu ◽  
Xingzhu Wang ◽  
Jie Liu
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Vector ◽  
Spectral Feature ◽  
Principal Component ◽  
Learning Approaches ◽  
Spectral Classification ◽  
Spectral Processing ◽  
Stellar Spectra ◽  
Gradient Based

With the rapid growth in astronomical spectra produced by large sky survey telescopes, traditional manual classification processes can no longer fulfill the requirements of precision and efficiency of spectral classification. There is an urgent need to employ machine learning approaches to conduct automated spectral classification tasks. Feature extraction is a critical step which has a great impact on any classification result. In this paper, a novel gradient-based method together with principal component analysis is proposed for the extraction of partial features of stellar spectra, that is, a feature vector indicating obvious local changes in data, which corresponds to the element line positions in the spectra. Furthermore, a general feature vector is utilized as an additional characteristic centering on the overall tendency of spectra, which can indicate stellar effective temperature. The two feature vectors and raw data are input into three neural networks, respectively, for training and each network votes for a predicted category of spectra. By selecting the class having the maximum votes, different types of spectra can be classified with high accuracy. The experimental results prove that a better performance can be achieved using the partial and general methods in this paper. The method could also be applied to other similar one-dimensional spectra, and the concepts proposed could ultimately expand the scope of machine learning application in astronomical spectral processing.

scholarly journals Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8114
Author(s):  
Atik Faysal ◽  
Wai Keng Ngui ◽  
Meng Hee Lim ◽  
Mohd Salman Leong
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Training Data ◽  
Learning Ability ◽  
Test Accuracy ◽  
Mode Decomposition ◽  
Machinery Fault Diagnosis

Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition (NEEEMD) was used for fault feature extraction. A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. A sample size of 64×64×3 pixels RGB scalograms are used as the classifier input. However, CNN requires a large number of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD), and continuous wavelet transform (CWT) are classified. The effectiveness of scalograms is also validated by comparing the classifier performance using grayscale samples from the raw vibration signals. All the outputs from bearing and blade fault classifiers showed that scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity, and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to further increase the CNN classifier’s performance and identify the optimal number of training data. After training the classifier using augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The proposed method can be used as a more generalized and robust method for rotating machinery fault diagnosis.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

scholarly journals Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis”

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 532
Author(s):  
Mohand Djeziri ◽  
Marc Bendahan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Special Issue ◽  
Maintenance Strategies ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault Diagnosis ◽  
Failure Prognosis ◽  
Fault Diagnosis And Prognosis

Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

2021 ◽  
Vol 63 (8) ◽  
pp. 465-471
Author(s):  
Shang Zhiwu ◽  
Yu Yan ◽  
Geng Rui ◽  
Gao Maosheng ◽  
Li Wanxiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Extraction Method ◽  
Dynamic Time Warping ◽  
Spectral Feature ◽  
Time Warping ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

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