Integration of MBSE into Mining Industry: Predictive Maintenance System

Guerroum, Mariya; Zegrari, Mourad; Amalik, Hafsa; Elmahjoub, Abdelhafid Ait

doi:10.46338/ijetae0422_21

Cited by 4 publications

(2 citation statements)

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“…In addition, there is an ambition to develop and redefine spare parts acquisition strategy and optimize maintenance activities by incorporating Predictive maintenance into the mining facilities. This goal will enable us to fulfill the specifications listed in the modeling of the authors' PdMSys, a predictive maintenance system for mining facilities [19].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Technics for Remaining useful Life Prediction using Diagnosis Data: a Case Study of a Jaw Crusher

Guerroum¹,

Zegrari²,

Masmoudi³

et al. 2022

IJETAE

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Predictive maintenance currently involves digital transformation with all the technologies developed to serve the latter. This maintenance strategy is believed to be an efficient solution to end late/early intervention issues. It is for this reason that machine health state monitoring by Remaining Useful Life prognosis is very crucial. However, in the literature, most studies focus on failure diagnosis more than the system's Remaining Useful Life. In addition, to prepare models to serve the prognosis, the use of actual machinery data is critical to assure the later scalability of the application. The literature about predictive maintenance has often evaluated data-driven approaches with machine learning techniques processing simulated Data rather than real ones. To tackle this problem, the authors propose a continuity of previous work treating a jaw crusher default diagnosis in the context of the ore mining industry. The RUL of the crusher components is estimated upon completion of the fault diagnosis data. Smart sensors Data have been preprocessed to serve the evaluation of four regression machine learning models: Bayesian Linear Regression, Poisson Regression, Neural Network Regression, and Random Forest. Poisson regression and Neural Network required data normalization in this case study to improve their performance. Linear regression methods proved their inability to forecast the machine degradation state, while the bagging ensemble method, Random Forest, was able to track the actual values. This paper aims to enhance the Prognosis and Health Management of the machine while contributing to the literature enhancement on failure prognosis using real industrial data. Keywords— Data-driven approach, Industry 4.0, Machine learning, Predictive Maintenance, RUL prognosis, Smart sensors.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Technics for Remaining useful Life Prediction using Diagnosis Data: a Case Study of a Jaw Crusher

Guerroum¹,

Zegrari²,

Masmoudi³

et al. 2022

IJETAE

Self Cite

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“…Predictive maintenance is rapidly growing, as well as in the raw material sector, including mining machines for excavating, drilling, transporting, processing (sieving and crushing), ventilation, and related technologies [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. For example, the developments in vibration-based diagnostics of gear and bearings used in belt conveyors are given in a review [ 20 ].…”

Section: State Of the Artmentioning

confidence: 99%

Condition Monitoring of Horizontal Sieving Screens—A Case Study of Inertial Vibrator Bearing Failure in Calcium Carbonate Production Plant

et al. 2023

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Predictive maintenance is increasingly popular in many branches, as well as in the mining industry; however, there is a lack of spectacular examples of its practice efficiency. Close collaboration between Omya Group and Wroclaw University of Science and Technology allowed investigation of the failure of the inertial vibrator’s bearing. The signals of vibration are captured from the sieving screen just before bearing failure and right after repair, when it was visually inspected after replacement. The additional complication was introduced by the loss of stable attachment of the vibrator’s shield, which produced great periodical excitation in each place of measurement on the machine. Such anomalies in the signals, in addition to falling pieces of material, made impossible the diagnostics by standard methods. However, the implementation of advanced signal processing techniques such as time–frequency diagrams, envelope spectrum, cyclic spectral coherence, orbits analysis, and phase space plots allowed to undermine defects (pitting on the inner ring). After repair, the amplitudes of vibration from the damaged bearing side were reduced by five times, while sound pressure was only two times lower. The quantitative parameters of vibrations showed significant changes: time series RMS (−68%) median energy of spectrograms (89%), frequencies ratio of cyclic spectral coherence (−85%), and average amplitude of harmonics in envelope spectrum (−80%). The orbits demonstrated changes in inclination angle (16%) and sizes (−48 ... −96%), as well as phase space plots sizes (−28 ... −67%). Directions of further research are considered.

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