Dependability of wireless sensor networks for industrial prognostics and health management

Elghazel, Wiem; Bahi, Jacques M.; Guyeux, Christophe; Hakem, Mourad; Medjaher, Kamal; Zerhouni, Noureddine

doi:10.1016/j.compind.2014.10.004

Cited by 51 publications

(40 citation statements)

References 67 publications

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“…As highlighted by Elghazel and al. [26], the way to consider operating conditions, especially future ones in RUL estimation, still needs deep studying. So we assume in this paper that each needed RUL value is known and that RUL evolution depends on the operating conditions, that is on the running profile in which the machine is used.…”

Section: B Controlled Running Profilesmentioning

confidence: 99%

Prognostic Decision Making to extend a platform useful life under service constraint

Herr

Nicod

Varnier

2014

2014 International Conference on Prognostics and Health Management

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This paper adresses the problem of optimizing the useful life of a heterogeneous distributed platform which has to produce a given production service. The purpose is to provide a production scheduling that maximizes the production horizon. The use of Prognostics and Health Management (PHM) results in the form of Remaining Useful Life (RUL) allows to adapt the schedule to the wear and tear of equipment. This work comes within the scope of Prognostics Decision Making (DM). Each considered machine is supposed to be able to provide several throughputs corresponding to different operating conditions. The key point is to select the appropriate profile for each machine during the whole useful life of the platform. Many heuristics are proposed to cope with this decision problem and are compared through simulation results. Simulations assess the efficiency of these heuristics. Distance to the theoretical maximal value comes close to 10% for the most efficient ones. A repair module performing a revision of the schedules provided by the heuristics is moreover proposed to enhance the results. First results are promising.

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Section: B Controlled Running Profilesmentioning

confidence: 99%

Prognostic Decision Making to extend a platform useful life under service constraint

Herr

Nicod

Varnier

2014

2014 International Conference on Prognostics and Health Management

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“…On the other hand, the main advantage of indirect damage detection is its lower cost for hardware and installation and the challenge of this detection method is the reliability and accuracy [2,3]. In recent years, the increasing demand of control and monitoring of structures in industry has caused an evolution on how system maintenance is realized and as a consequence, the aforementioned challenges are being overcome, while SHM approaches are gaining interest [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Wireless sensor nodes for generic signal conditioning: Application to Structural Health Monitoring of wind turbines

Herrasti

Val

Gabilondo

et al. 2016

Sensors and Actuators A: Physical

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“…The core criterion of digital image correlation is to search for predefined patterns in sequential images. Zero mean normalized cross-correlation and peak finding have demonstrated high resolution for remote inspection [22,23]. The quantitative comparisons in the temporal domain and frequency domain have shown the experimental accuracy of the self-developed vibration monitoring system (VMS) system.…”

Section: Introductionmentioning

confidence: 99%

In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology

Chang

Huang

2019

Sensors

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This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms after the image calibration and the benchmark of a laser displacement sensor (LDS). The embedded program of machine vision has used zero-mean normalized correlation (ZNCC) and peak finding (PF) for tracking the registered characteristics on the object surface. The calibrated VMS provides time–displacement curves related to both horizontal and vertical directions, promising remote inspections of selected points without attaching additional markers or sensors. The experimental setup of the VMS is cost-effective and uncomplicated, supporting universal combinations between the imaging system and computational devices. The procedures of the proposed scheme are (1) setting up a digital camera, (2) calibrating the imaging system, (3) retrieving the data of image streaming, (4) executing the ZNCC criteria, and providing the time–displacement results of selected points. The experiment setup of the proposed VMS is straightforward and can cooperate with surveillances in industrial environments. The embedded program upgrades the functionality of the camera system from the events monitoring to remote measurement without the additional cost of attaching sensors on motors or targets. Edge nodes equipped with the image-tracking program serve as the physical layer and upload the extracted features to a cloud server via the wireless sensor network (WSN). The VMS can provide customized services under the architecture of the cyber–physical system (CPS), and this research offers an early warning alarm of the mechanical system before unexpected downtime. Based on the smart sensing technology, the in situ diagnosis of industrial motors given from the VMS enables preventative maintenance and contributes to the precision measurement of intelligent automation.

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Dependability of wireless sensor networks for industrial prognostics and health management

Cited by 51 publications

References 67 publications

Prognostic Decision Making to extend a platform useful life under service constraint

Prognostic Decision Making to extend a platform useful life under service constraint

Wireless sensor nodes for generic signal conditioning: Application to Structural Health Monitoring of wind turbines

In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology

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