Physics-Based Convolutional Neural Network for Fault Diagnosis of Rolling Element Bearings

Sadoughi, Mohammadkazem; Hu, Chao

doi:10.1109/jsen.2019.2898634

Cited by 133 publications

(44 citation statements)

References 29 publications

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“…The available literature presents a considerable number of studies with solutions for metalworking. These include the detection of defects in metal casting [5,10], recognition of slab identification [22], prediction of mechanical properties [38], bearing fault diagnosis [14,19,29,35,41], and steel defect identification [25,28,31]. On the other hand, there are only a few works that apply image processing to measure the strip position in a rolling mill process.…”

Section: Introductionmentioning

confidence: 99%

Automatic monitoring of steel strip positioning error based on semantic segmentation

Lemos

Silva

Nagy

2020

Int J Adv Manuf Technol

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The misalignment of steel strips in relation to the roller table centerline still is an impairment for the rolling mill production lines. Nowadays, the strip position correction remains largely in the purview of human analysis, in which the strip steering is traditionally a semi-manual operation. Automating the alignment process could reduce the maintenance costs, damage to the plant, and prevent material losses. The first step into the automatization is to determine the strip position and its referred error. This study presents a method that employs semantic segmentation based on convolution neural networks to estimate steel strips positioning error from images of the process. Additionally, the system mitigates the influences of mechanical vibration on the images. The system performance was assessed by standard semantic segmentation evaluation metrics and in comparison with the dataset ground truth. The results showed that 97% of the estimated positioning errors are within a 2-pixel margin. The method demonstrated to be a robust real-time solution as the networks were trained from a set of low-resolution images acquired in a complex environment.

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Section: Introductionmentioning

confidence: 99%

Automatic monitoring of steel strip positioning error based on semantic segmentation

Lemos

Silva

Nagy

2020

Int J Adv Manuf Technol

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“…Hybrid architectures which use physics-based preprocessing have found applications in manufacturing industry [166]- [169], healthcare industry [170]- [172], energy industry [173], and for processing hydrological data [174]- [176], meteorological data [177] among several others. Compressed sensing was used for extracting low dimensional features from raw temporal data before passing it into a stacked denoising autoencoder DNN [178].…”

Section: A Physics Based Preprocessing (Pbp)mentioning

confidence: 99%

Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

2020

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A multitude of cyber-physical system (CPS) applications, including design, control, diagnosis, prognostics, and a host of other problems, are predicated on the assumption of model availability. There are mainly two approaches to modeling: Physics/Equation based modeling (Model-Based, MB) and Machine Learning (ML). Recently, there is a growing consensus that ML methodologies relying on data need to be coupled with prior scientific knowledge (or physics, MB) for modeling CPS. We refer to the paradigm that combines MB approaches with ML as hybrid learning methods. Hybrid modeling (HB) methods is a growing field within both the ML and scientific communities, and are recognized as an important emerging but nascent area of research. Recently, several works have attempted to merge MB and ML models for the complete exploitation of their combined potential. However, the research literature is scattered and unorganized. So, we make a meticulous and systematic attempt at organizing and standardizing the methods of combining ML and MB models. In addition to that, we outline five metrics for the comprehensive evaluation of hybrid models. Finally, we conclude by shedding some light on the challenges of hybrid models, which we, as a research community, should focus on for harnessing the full potential of hybrid models. An additional feature of this survey is that the hybrid modeling work has been discussed with a focus on modeling cyber-physical systems. INDEX TERMS Cyber-physical systems, deep learning, deep neural networks, hybrid models, model-based, machine learning, physics guided, physics informed, physics prior, theory guided.

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“…Table 2 shows a chromosome in DPGA, which is implemented by a one-dimensional list consisting of categorical and continuous variables to represent algorithm-selection and parameter-specification. Specifically, it is composed of four parts corresponding to data rebalancing, feature extraction, feature reduction, and learning subtasks as follows: In case of SVM: The penalty parameter C ∈ [1][2][3][4][5][6][7][8] In case of RF: The number of trees n tree ∈ [2-10]…”

Section: Chromosome Representationmentioning

confidence: 99%

“…Various approaches have been proposed in each problem and they can be divided into three categories: Physics-based, data-driven, and hybrid-based approaches. Physics-based approaches incorporate prior system-specific knowledge from an expert, as shown in previous studies, of fault-type classification [2][3][4][5] and RUL prediction [6][7][8][9] problems. Alternatively, data-driven approaches are based on statistical-/machine-learning techniques using the historical data (see example studies about aero-propulsion system simulation (C-MAPSS) dataset) problems.…”

Section: Introductionmentioning

confidence: 99%

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

Trinh

Kwon

2020

Applied Sciences

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Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems.

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Physics-Based Convolutional Neural Network for Fault Diagnosis of Rolling Element Bearings

Cited by 133 publications

References 29 publications

Automatic monitoring of steel strip positioning error based on semantic segmentation

Automatic monitoring of steel strip positioning error based on semantic segmentation

Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

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