Technology development and commercial applications of industrial fault diagnosis system: a review

Liu, Chengze; Cichoń, A.; Królczyk, Grzegorz; Li, Zhixiong

doi:10.1007/s00170-021-08047-6

Cited by 31 publications

(18 citation statements)

References 198 publications

(303 reference statements)

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“…Neural networks are used for fault diagnosis, the verification of technical equipment, image recognition, and industrial fault diagnosis systems [ 15 , 16 , 17 , 18 ]. There are several types of Convolutional Neural Networks (CNNs): GoogLeNet [ 19 , 20 , 21 ], ResNet50 [ 22 , 23 , 24 ], and EfficientNet-b0 [ 25 , 26 , 27 ].…”

Section: Thermographic Fault Diagnosis Techniquementioning

confidence: 99%

Thermographic Fault Diagnosis of Shaft of BLDC Motor

Głowacz

2022

Sensors

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A technique of thermographic fault diagnosis of the shaft of a BLDC (Brushless Direct Current Electric) motor is presented in this article. The technique works for the shivering of the thermal imaging camera in the range of 0–1.5 [m/s2]. An electric shaver was used as the source of the BLDC motor. The following states of the BLDC motor were analyzed: Healthy BLDC motor (HB), BLDC motor with one faulty shaft (1FSB), BLDC motor with two faulty shafts (2FSB), and BLDC motor with three faulty shafts (3FSB). A new method of feature extraction named PNID (power of normalized image difference) was presented. Deep neural networks were used for the analysis of thermal images of the faulty shaft of the BLDC motor: GoogLeNet, ResNet50, and EfficientNet-b0. The results of the proposed technique were very good. PNID, GoogLeNet, ResNet50, and EfficientNet-b0 have an efficiency of recognition equal to 100% for four classes.

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Section: Thermographic Fault Diagnosis Techniquementioning

confidence: 99%

Thermographic Fault Diagnosis of Shaft of BLDC Motor

Głowacz

2022

Sensors

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“…With the progress of various mechanical equipment, equipment failure will lead to a prolonged industrial fault, which will result in economic losses. Therefore, rolling bearing fault diagnostics has become a popular study area (Liu et al, 2022). The traditional rolling bearing fault diagnosis process involves signal acquisition and feature extraction (Prieto et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on deep convolutional neural network and gated recurrent unit

Zhou

Wang

et al. 2023

JAMDSM

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Rolling bearing is an essential part of various rotating machines, and its signal is the typical nonlinear signal. Traditional fault diagnosis usually relies on manual experience to extract the features of signals first. Deep convolutional neural networks (DCNN) can make fuller use of time series than traditional convolutional neural networks (CNN). Because of the low accuracy rate, fault diagnosis using gated recurrent unit (GRU) alone is not unsatisfactory. In order to improve the temporality of one-dimensional convolutional neural networks (1D-CNN) and enhance the accuracy of GRU, a novel fault diagnosis method called deep convolutional neural networksgated recurrent unit (DCNN-GRU) is first put forward, which combines DCNN with GRU. The original signals without preprocessing are input into the DCNN, and the outputs of DCNN are input into the GRU consequently. Then the faults of rolling bearing can be diagnosed effectively. As the post-processing method, the t-distributed stochastic neighbor embedding (t-SNE) method is applied to visualize the fault diagnosis results. Six different network models, including the DCNN-GRU, are used to train the same fault dataset for comparison. The simulation results show that the proposed method can reach more than 99.9% accuracy stably for the given dataset, which can verify the feasibility and effectiveness of proposed method. And the DCNN-GRU can also be verified with good generalization ability using different dataset.

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“…The modernization and automation of the manufacturing industry involves the use of increasingly complex, interdependent (often interconnected), precise and fast mechanical equipment. The onset of a defect [ 14 ], if not properly identified [ 15 ], can cause cascading component failures [ 16 ], causing economic damage [ 17 ], financial losses [ 18 ] and industry downtime [ 19 ]. However, for the evolved ML and DL algorithms to provide reliable and trustworthy information on the monitored process, data provided by interconnected sensors must be truly accurate and traceable.…”

Section: Introductionmentioning

confidence: 99%

Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings

et al. 2023

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In the field of vibration monitoring and control, the use of low-cost multicomponent MEMS-based accelerometer sensors is nowadays increasingly widespread. Such sensors allow implementing lightweight monitoring systems with low management costs, low power consumption and a small size. However, for the monitoring systems to provide trustworthy and meaningful data, the high accuracy and reliability of sensors are essential requirements. Consequently, a metrological approach to the calibration of multi-component accelerometer sensors, including appropriate uncertainty evaluations, are necessary to guarantee traceability and reliability in the frequency domain of data provided, which nowadays is not fully available. In addition, recently developed metrological characterizations at the microscale level allow to provide detailed and accurate quantification of the enhanced technical performance and the responsiveness of these sensors. In this paper, a dynamic calibration procedure is applied to provide the sensitivity parameters of a low-cost, multicomponent MEMS sensor accelerometer prototype (MDUT), designed, developed and realized at the University of Siena, conceived for rolling bearings vibration monitoring in a broad frequency domain (from 10 Hz up to 25 kHz). The calibration and the metrological characterization of the MDUT are carried out by comparison to a reference standard transducer, at the Primary Vibration Laboratory of the National Institute of Metrological Research (INRiM).

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Technology development and commercial applications of industrial fault diagnosis system: a review

Cited by 31 publications

References 198 publications

Thermographic Fault Diagnosis of Shaft of BLDC Motor

Thermographic Fault Diagnosis of Shaft of BLDC Motor

Fault diagnosis of rolling bearing based on deep convolutional neural network and gated recurrent unit

Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings

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