Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion

Huang, Min; Liu, Zhen; Yang, Tao

doi:10.1016/j.simpat.2019.101981

Cited by 112 publications

(54 citation statements)

References 145 publications

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“…The idea behind IIoT is that it enables an increased level of automation and better overall understanding of processes, leading to improved efficiency and thus profitability. In smart factories and manufacturing, sensor networks are used to provide data for ML applications, cyber-physical systems, and digital twins, for example, for managing manufacturing processes or supply chains [ 2 , 35 , 55 , 56 ]. Such applications provide frameworks for predictive analysis for maintenance, prognostics, and decision-making.…”

Section: Use Case Examplesmentioning

confidence: 99%

Metrological Challenges in Collaborative Sensing: Applicability of Digital Calibration Certificates

Mustapää

Nikander

Hutzschenreuter

et al. 2020

Sensors

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IoT systems based on collaborative sensor networks are becoming increasingly common in various industries owing to the increased availability of low-cost sensors. The quality of the data provided by these sensors may be unknown. For these reasons, advanced data processing and sensor network self-calibration methods have become popular research topics. In terms of metrology, the self-calibration methods lack the traceability to the established measurement standards of National Metrology Institutes (NMIs) through an unbroken chain-link of calibration. This problem can be solved by the ongoing digitalization of the metrology infrastructure. We propose a conceptual solution based on Digital Calibration Certificates (DCCs), Digital SI (D-SI), and cryptographic digital identifiers, for validation of data quality and trustworthiness. The data that enable validation and traceability can be used to improve analytics, decision-making, and security in industrial applications. We discuss the applicability and benefits of our solutions in a selection of industrial use cases, where collaborative sensing has already been introduced. We present the remaining challenges in the digitization and standardization processes regarding digital metrology and the future work required to address them.

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Section: Use Case Examplesmentioning

confidence: 99%

Metrological Challenges in Collaborative Sensing: Applicability of Digital Calibration Certificates

Mustapää

Nikander

Hutzschenreuter

et al. 2020

Sensors

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show abstract

“…In other words, different types of sensors can provide different symptoms when the components fail. Fusion in feature domain and frequency domain are also discussed in other studies [ 42 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Vibration Signals Applications

Chen

Lee

2021

Sensors

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This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

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“…Due to the equipment's power frequency vibration, electrical noise, hydraulic system pulsation noise, and noise interference in transmission link modulation, the features in online monitoring system is weak and the feature extraction method is sensitive to the interference factors. The solution for mechanical fault diagnosis has become a hot topic in the field of mechanical design 3,4 …”

Section: Introductionmentioning

confidence: 99%

“…The solution for mechanical fault diagnosis has become a hot topic in the field of mechanical design. 3,4 By using artificial intelligent algorithms to monitor and predict running state of mechanical system, the mechanical fault diagnostic system can automatically make reasonable maintenance decisions. The smart mechanical diagnosis gets rid of the dilemma of traditional fault diagnosis methods, overly relying on fault mechanisms, diagnosis experts, and professional and technical personnel.…”

Section: Introductionmentioning

confidence: 99%

Automatic machinery fault detection via using distributed sensor information

Chen

Huo

2021

Internet Technology Letters

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It is critical in intelligent manufacturing that monitors devices to sustain normal status. Recently, the artificial intelligence‐powered diagnosis is the key in smart monitoring process and has become a hot topic in the engineering field. In previous diagnostic methods, complete failure samples are the premise to activate the intelligent diagnostic model. However, the actual failures of the mechanical diagnostic are far more than that we can obtain under running state in advance. In order solve this issue, this paper adopts Generative Adversarial Networks (GAN) by using simulated data to obtain complete failure samples, which builds a bridge between artificial intelligent model and mechanical fault diagnostic system. In mechanical diagnostic system, we first use finite element simulation to generate the missing fault samples. The generated and existing fault samples are supplemented each other to construct complete failure samples. The complete failure samples are used as training set to train an intelligent model which is used to predict the status for future status of mechanical system. The status of mechanical system is represented as the signal from distributed sensors. The fault diagnostic system is verified on a public mechanical system dataset and the results demonstrate it is superior to previous ones.

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Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion

Cited by 112 publications

References 145 publications

Metrological Challenges in Collaborative Sensing: Applicability of Digital Calibration Certificates

Metrological Challenges in Collaborative Sensing: Applicability of Digital Calibration Certificates

Deep Learning Approach for Vibration Signals Applications

Automatic machinery fault detection via using distributed sensor information

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