A new Local-Global Deep Neural Network and its application in rotating machinery fault diagnosis

Zhao, Xiaoli; Jia, Minping

doi:10.1016/j.neucom.2019.08.010

Cited by 60 publications

(31 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where β is the norm adjustment parameter. In the study by Zhao et al 33 and Sun et al 35 the training process of the ISAE is the same as the RBM by using the greedy layer-by-layer pertraining. The ISAE (as described in Fig.…”

Section: Intelligent Fault Recognition Algorithm: Improved Stacked Aumentioning

confidence: 99%

“…As one of the most advanced data processing and pattern recognition methodologies, deep learning (DL) has been broadly applied in many fields such as machine vision, speech processing and intelligent fault diagnosis. [28][29][30][31][32][33][34][35] Among them, auto-encoder (AE) 34 is one of the more widely applied deep learning architectures that can effectively extract the underlying data into high-level and meaningful features. For instance, Sun et al proposed a novel induction motor fault diagnosis based on a sparse auto-encoder (SAE).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Intelligent Weak Fault Recognition Framework for Rotating Machinery

Zhao¹,

Jia²,

Ding³

et al. 2020

The International Journal of Acoustics and Vibration

Self Cite

View full text Add to dashboard Cite

The presence of strong background noises makes it a challenging task to detect weak fault characteristics in vibration signals collected from rotating machinery. Thus, a two-stage intelligent weak fault recognition framework, which includes signal enhancement and intelligent recognition, is proposed in this work. The signal enhancement is accomplished via an optimized relevant variational mode decomposition (ORVMD) algorithm. Specifically, the optimal parameters are derived by combining a particle swarm optimization (PSO) algorithm and the novel defined relevant energy (Re) index. This optimized VMD algorithm can extract the principal components from the raw signals. Then, the enhanced vibration signals via the proposed ORVMD are converted into spectral signals and fed into an improved stacked auto-encoder (ISAE) algorithm for fault recognition. Experimental results demonstrate the effectiveness of the proposed algorithms and fault diagnosis framework in rotating machinery fault recognition and detection.

show abstract

Section: Intelligent Fault Recognition Algorithm: Improved Stacked Aumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Intelligent Weak Fault Recognition Framework for Rotating Machinery

Zhao¹,

Jia²,

Ding³

et al. 2020

The International Journal of Acoustics and Vibration

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a newcomer in the field of intelligent fault diagnosis, deep learning has received great attention in recent years [2], [21], [24], [27]. The purpose of deep learning is to autonomously mine valuable information hidden in massive measurement and monitoring data through multiple layers of repeated nested feature transformation and feature learning, and it is used to establish the accurate mapping relationship between the device and its operating state through data and models.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Framework of Rolling Bearing Using Adaptive Sparse Contrative Auto-Encoder With Optimized Unsupervised Extreme Learning Machine

2020

Self Cite

View full text Add to dashboard Cite

Nowadays, the intelligent fault diagnosis based on deep learning have achieved remarkable results in the fields of the industrial equipment health monitoring and management. To implement the adaptive feature extraction and fault isolation for key components of rotating machinery (rolling bearings, etc.), two new algorithms, Adaptive Sparse Contrative Auto-encoder (ASCAE) algorithm and Optimized Unsupervised Extreme Learning Machine (OUSELM) classifier by Cuckoo Search Algorithm (CSA), can be firstly designed in this paper, respectively. Furthermore, a new rolling bearing fault diagnosis framework based on ASCAE combined with OUSELM is first of all proposed in this paper. Accordingly, this designed fault diagnosis framework can be divided into three main steps: i). Firstly, the vibration signals of rolling bearings can be collected and processed on the key components of rotating machinery, and then the collected vibration signals can be accordingly converted into frequency signals; ii). Secondly, the transformed spectral signals can be entered into the constructed ASCAE for feature learning to exploit the multi-layer sensitive features from the hidden raw data; iii). Thirdly, the extracted multi-layer sensitive features can be flowed into the trained OUSELM classifier for unsupervised fault state separation and diagnosis. More specifically, our designed fault diagnosis framework (ASCAE-OUSELM) can employ the homotopy regularization theory, sparse theory, intelligent optimization algorithm and other tools to optimize the parameters and improve the performance of the original Contrative Auto-encoder (CAE) algorithm and Unsupervised Extreme Learning Machine (USLEM) algorithm, respectively. At the same time, the proposed fault diagnosis framework can achieve effective sparse and sensitive feature information extraction in the feature extraction stage (ASCAE) to avoid over-fitting. In fault isolation stage, the issue of the supervised and low training efficiency caused by traditional deep learning model can be perfectly addressed by OUSELM. Eventually, the experimental data of rolling bearings validated the effectiveness of the proposed fault diagnosis framework and two deigned algorithms. INDEX TERMS Rolling bearings, fault diagnosis framework, homotopy regularization, Adaptive Sparse Contrative Auto-encoder (ASCAE), Optimized Unsupervised extreme learning machine (OUSELM), Cuckoo Search Algorithm (CSA).

show abstract

“…Scholars have used MLP-BP and its corresponding deeplearning methods to evaluate long-distance pipelines (Zhao and Jia 2019;Nguyen et al 2006). Xie and Xing (2017) used the gray correlation method to analyze the 7 influencing factors affecting the wax deposition rate of pipelines and established a 7-100 back-propagation (BP) neural network prediction model.…”

Section: Introductionmentioning

confidence: 99%

Standard friction prediction model of long-distance hot oil pipelines

Yao

et al. 2020

Pet. Sci.

View full text Add to dashboard Cite

We developed a predictive model for the pipeline friction in the 520-730 m 3 /h transmission range using the multi-layerperceptron-back-propagation (MLP-BP) method and analyzing the unit friction data after the pigging of a hot oil pipeline. In view of the shortcomings of the MLP-BP model, two optimization methods, the genetic algorithm (GA) and mind evolutionary algorithm (MEA), were used to optimize the MLP-BP model. The research results were applied to the standard friction prediction of three sections of a hot oil pipeline. After the GA and MEA optimizations, the average errors of the three sections were 0.0041 MPa for the GA and 0.0012 MPa for the MEA, and the mean-square errors were 0.083 and 0.067, respectively. The MEA-BP model prediction results were characterized by high precision and small dispersion. The MEA-BP prediction model was applied to the analysis of the wax formation 60 and 90 days after pigging. The analysis results showed that the model can effectively guide pipe pigging and optimization. There was little sample data for the individual transmission and oil temperature steps because the model was based on actual production data modeling and analysis, which may have affected the accuracy and adaptability of the model.

show abstract

A new Local-Global Deep Neural Network and its application in rotating machinery fault diagnosis

Cited by 60 publications

References 33 publications

A New Intelligent Weak Fault Recognition Framework for Rotating Machinery

A New Intelligent Weak Fault Recognition Framework for Rotating Machinery

Fault Diagnosis Framework of Rolling Bearing Using Adaptive Sparse Contrative Auto-Encoder With Optimized Unsupervised Extreme Learning Machine

Standard friction prediction model of long-distance hot oil pipelines

Contact Info

Product

Resources

About