A deep contractive auto-encoding network for machinery fault diagnosis

Jiao, Yi; Fu, Sibao; Cui, Shujie; Zhao, Chenglin

doi:10.1109/iscit.2018.8587983

Cited by 5 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, we trained a number of ML classification methods, including bagging and boosting‐based classifiers based on the feature representations extracted from the stacked contractive autoencoder and combined their outputs using the stack generalization framework. This combination helps to simultaneously mitigate the problems of under‐fitting (very simple models) and over‐fitting (very complex models) 30,31 …”

Section: Methodsmentioning

confidence: 99%

Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization

Cai

Tafti

Ngufor

et al. 2021

Cardiovasc electrophysiol

View full text Add to dashboard Cite

Introduction The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders. Methods We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response. Results We compared the performance of EoE against traditional ML methods and the state‐of‐the‐art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1‐score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results. Conclusion The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high‐risk patients.

show abstract

Section: Methodsmentioning

confidence: 99%

Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization

Cai

Tafti

Ngufor

et al. 2021

Cardiovasc electrophysiol

View full text Add to dashboard Cite

show abstract

“…The superiority of the proposed method has been validated with experiments with a test accuracy of 99.83% [15]. For diagnosing the machinery fault, a deep contractive auto-encoding network (DCAEN) model has been presented in [16]. The DCAEN model is constructed by unsupervised learning named contractive auto-encoder (CAE), which helps extract the features unsupervised.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the DCAEN model can extract the features automatically from the raw data without creating artificial features. Finally, the proposed model in [16] has been validated through experiments that resulted in an accuracy of 99.60% on the rolling bearing dataset, which is higher than the stateof-the-art methods. In [17], fault diagnosis of rotating machinery has been made by developing an enhancement deep feature fusion method.…”

Section: Related Workmentioning

confidence: 99%

Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series

Yakkati,

Yeduri,

Tripathy

et al. 2023

IEEE Access

View full text Add to dashboard Cite

In the industry, machinery failure causes catastrophic accidents and destructive damage to the machines. It causes the machinery to stop and reduces production, causing financial losses to the industry. As a result, identifying machine faults at an early stage is critical. With the rapid advancement in artificial intelligence-based methods, developing automated systems that can diagnose machinery faults is necessary and challenging. This paper proposes a multi-channel time-frequency domain deep convolutional neural network (CNN)-based approach for machinery fault diagnosis using multivariate time-series data from multisensors (tachometer, microphone, underhang bearing accelerometer, and overhand bearing accelerometer). The wavelet synchro-squeezed transform (WSST) based technique is used to evaluate the time-frequency images from the multivariate time-series data. The time-frequency images are fed into the multi-channel deep CNN model for automated fault detection. The proposed multi-channel deep CNN model is multi-headed, considering the time-frequency domain information of each channel time-series data for automated fault detection. The proposed model's performance is compared to benchmark models regarding testing accuracy, total parameters, and model size. Experiments have shown that the proposed model outperforms benchmark models regarding classification accuracy. The proposed multi-channel CNN model has obtained the accuracy and F1-score values of 99.48% and 99% for fault classification using timefrequency images of multi-sensor data. Finally, the proposed model's performance is measured regarding inference time when deployed on edge computing devices such as the Raspberry Pi and the Nvidia Jetson AGX Xavier. INDEX TERMS Deep CNN, Machinery failure, Nvidia Jetson AGX Xavier, Raspberry Pi, time-frequency analysis, and wavelet synchro-squeezed transform.

show abstract

Comparison of CWRU Dataset-Based Diagnosis Approaches: Review of Best Approaches and Results

Wei

Söffker

2021

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

A deep contractive auto-encoding network for machinery fault diagnosis

Cited by 5 publications

References 8 publications

Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization

Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization

Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series

Comparison of CWRU Dataset-Based Diagnosis Approaches: Review of Best Approaches and Results

Contact Info

Product

Resources

About