A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors

Toma, Rafia Nishat; Piltan, Farzin; Kim, Jong‐Myon

doi:10.3390/s21248453

Cited by 29 publications

(12 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, current transducers can be used to measure the stator current from a single input source if frequency inverters and current transformers are not available. In addition to being highly reliable and noninvasive, motor current signal analysis (MCSA) is also considered one of the most effective condition monitoring methods in bearing fault diagnosis [14][15][16]. MCSA has been applied to both to analyze bearing faults and the fault severity in IMs with fault frequency analysis [1,17].…”

Section: Introductionmentioning

confidence: 99%

Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features

Toma

Gao

Piltan

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extraction techniques are sensitive to the noisy component of the signal and need more time for training. To deal with these issues, a comparatively new feature extraction technique, referred to as a wavelet scattering transform (WST) is utilized, and incorporated with ML classifiers to design a framework for bearing fault classification in this paper. The WST is a knowledge-based technique, and the structure is similar to the convolution neural network. This technique provides low-variance features of real-valued signals, which are usually necessary for classification tasks. These signals are resistant to signal deformation and preserve information at high frequencies. The current signal data from a publicly available dataset for three different bearing conditions are considered. By combining the scattering path coefficients, the decomposition coefficients from the 0th and 1st layers are considered as features. The experimental results demonstrate that WST-based features, when used with ensemble ML algorithms, could achieve more than 99% classification accuracy. The performance of ANN models with these features is similar. This work exhibits that utilizing WST coefficients for the motor current signal as features can improve the bearing fault classification accuracy when compared to other feature extraction approaches such as empirical wavelet transform (EWT), information fusion (IF), and wavelet packet decomposition (WPD). Thus, our proposed approach can be considered as an effective classification method for the fault diagnosis of rotating machinery.

show abstract

Section: Introductionmentioning

confidence: 99%

Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features

Toma

Gao

Piltan

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, the encoder compresses the input vector

into a lower dimensional embedding

, with

; the decoder then tries to reconstruct

from

, producing an output vector

. They have been used for dimensionality reduction [ 57 , 58 , 59 ], classification [ 60 , 61 , 62 ], and anomaly detection [ 63 , 64 ].…”

Section: Methodsmentioning

confidence: 99%

“…embedding 𝑧 ∈ 𝑅 , with 𝑑 < 𝑛; the decoder then tries to reconstruct 𝑥 from 𝑧, producing an output vector 𝑥 ∈ 𝑅 . They have been used for dimensionality reduction [57][58][59], classification [60][61][62], and anomaly detection [63,64]. We implemented a convolutional autoencoder (CAE) coupled with an LSTM backend, following an idea similar to that described in [65]: first, the autoencoder was trained to learn good embeddings of the input data; then, we then passed these learned embeddings to an LSTM architecture like the one described in the above section.…”

Section: Cae-lstmmentioning

confidence: 99%

A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection

et al. 2023

View full text Add to dashboard Cite

Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the American Heart Association, approximately 19.1 million deaths were attributed to CVDs in 2020, in particular, ischemic heart disease and stroke. Several known risk factors for CVDs include smoking, alcohol consumption, lack of regular physical activity, and diabetes. The last decade has been characterized by widespread diffusion in the use of wristband-style wearable devices which can monitor and collect heart rate data, among other information. Wearable devices allow the analysis and interpretation of physiological and activity data obtained from the wearer and can therefore be used to monitor and prevent potential CVDs. However, these data are often provided in a manner that does not allow the general user to immediately comprehend possible health risks, and often require further analytics to draw meaningful conclusions. In this paper, we propose a disentangled variational autoencoder (β-VAE) with a bidirectional long short-term memory network (BiLSTM) backend to detect in an unsupervised manner anomalies in heart rate data collected during sleep time with a wearable device from eight heterogeneous participants. Testing was performed on the mean heart rate sampled both at 30 s and 1 min intervals. We compared the performance of our model with other well-known anomaly detection algorithms, and we found that our model outperformed them in almost all considered scenarios and for all considered participants. We also suggest that wearable devices may benefit from the integration of anomaly detection algorithms, in an effort to provide users more processed and straightforward information.

show abstract

“…However, in recent times, researchers extract features first and then apply them to the DL model to obtain high-level features to automatically classify the faults and improve model accuracy [31]. The DL-based fault diagnosis mainly consists of deep auto-encoders [32,33], a deep belief network (DBN) [34], and a convolution neural network (CNN) [28]. By using the time, frequency, and time-frequency domain features, Deng et al proposed a deep Boltzmann machine (DBM) for multiple fault classification of a rolling bearing with high reliability [35].…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions

Toma

Piltan

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution neural network (CNN) with the motor-current signal is proposed to classify bearing faults. In the beginning, we split the dataset into four parts, considering the operating conditions. Then, the original signal is segmented into multiple samples, and we apply the Gramian angular field (GAF) algorithm on each sample to generate two-dimensional (2-D) images, which also converts the time-series signals into polar coordinates. The image conversion technique eliminates the requirement of manual feature extraction and creates a distinct pattern for individual fault signatures. Finally, the resultant image dataset is used to design and train a 2-layer deep CNN model that can extract high-level features from multiple images to classify fault conditions. For all the experiments that were conducted on different operating conditions, the proposed method shows a high classification accuracy of more than 99% and proves that the GAF can efficiently preserve the fault characteristics from the current signal. Three built-in CNN structures were also applied to classify the images, but the simple structure of a 2-layer CNN proved to be sufficient in terms of classification results and computational time. Finally, we compare the experimental results from the proposed diagnostic framework with some state-of-the-art diagnostic techniques and previously published works to validate its superiority under inconsistent working conditions. The results verify that the proposed method based on motor-current signal analysis is a good approach for bearing fault classification in terms of classification accuracy and other evaluation parameters.

show abstract

A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors

Cited by 29 publications

References 61 publications

Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features

Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features

A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions

Contact Info

Product

Resources

About