Predictive maintenance in mining industry: grinding mill case study

Rihi, Ayoub; Baïna, Salah; Mhada, Fatima-zahra; Elbachari, Essaid; Tagemouati, Hicham; Guerboub, Mhamed; Benzakour, Intissar

doi:10.1016/j.procs.2022.09.306

Cited by 15 publications

(5 citation statements)

References 14 publications

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“…Rihi et al [19] focused on the mining industry and presented a case study of PdM for grinding mills. They demonstrated how machine learning techniques can be applied to predict failures in grinding mills, which play a critical role in ore processing.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Mechanisms for PdM in Automotive Machinery: A Comprehensive Analysis using ML/DL

Snehal A. Patil

2024

jes

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Predictive maintenance (PdM) technique involves analyzing and utilizing data to identify problems before they happen. It can help prevent costly repairs and downtime. In the past few years, the use of intelligent tools for PdM in automotive machinery has been increasing. These tools can be used to analyze and collect data from various sources, such as cloud computing and sensors. In the prediction of failures, this data can be used in combination with machine learning algorithms. With the help of advanced technologies, such as machine learning and sensors, PdM has become a viable option to maintain machinery while minimizing costs and downtime. The paper presents a comprehensive analysis of the various components of the intelligent tools that are used for PdM. It starts by exploring the different kinds of sensors and their functions in monitoring the condition of the equipment. The paper then explores the synergistic relationship between machine learning and data analytics, demonstrating how these technologies can help identify potential issues, predict the remaining useful life of the equipment, and detect early anomalies. The paper reviews the literature on the use of intelligent tools and sensors for PdM in automotive machinery. It delves into the diverse kinds of mechanisms that have been employed for this type of PdM, the pros and cons of using such tools, as well as the possible directions in this domain. Despite the various challenges that have been presented, the potential of implementing intelligent tools in automotive machinery is still immense. They can help prevent equipment downtime and improve the safety and efficiency of the operations of the machinery. As the technology matures, we can expect the adoption of such mechanisms to increase. The report emphasizes the significant contribution of intelligent tools and sensors to the optimization of the maintenance schedules and the reduction of unplanned downtime in automotive machinery. The findings of this study provide a roadmap for practitioners, researchers, and industrial organizations looking to harness the potential of such mechanisms to guarantee the longevity of their assets.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Mechanisms for PdM in Automotive Machinery: A Comprehensive Analysis using ML/DL

Snehal A. Patil

2024

jes

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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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“…For example, the early replacement of liners and preventive maintenance of the pinion gear of the mill can avoid a diminution in the grinding capacity induced by the maximum height of the load and the rotation speed. The loss of equipment and a full production stop can economically hinder the mine operation more than short stops for keeping components healthy [46]. However, identifying when the piece should be replaced is difficult as it depends on factors such as previous operating conditions and the ore's hardness and abrasive characteristics.…”

Section: Data As Input For Prediction: Predictive Modelsmentioning

confidence: 99%

“…The data collected from mills, such as the shell's vibration or acoustic emissions from installed sensors [47], can indicate the operational condition. ML tools can be applied to this data to implement maintenance advice in comminution plants to keep their stability [46].…”

Section: Data As Input For Prediction: Predictive Modelsmentioning

confidence: 99%

On the Challenges of Applying Machine Learning in Mineral Processing and Extractive Metallurgy

et al. 2023

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The application of Machine Learning in Mineral Processing and Extractive Metallurgy has important benefits in terms of increasing the predictability and controllability of the processes, optimizing their performance, and improving maintenance. However, this application has significant implementation challenges. This paper analyzes these challenges and proposes ways of addressing them. Among the main identified challenges are data scarcity and the difficulty in characterizing abnormal events/conditions as well as modeling processes, which require the creative use of different learning paradigms as well as incorporating phenomenological models in the data analysis process, which can make the learning process more efficient. Other challenges are related to the need of developing reliable in-line sensors, adopting interoperability data models and tools, and implementing the continuous measurement of critical variables. Finally, the paper stresses the need for training of advanced human capital resources with the required skills to address these challenges.

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Predictive maintenance in mining industry: grinding mill case study

Cited by 15 publications

References 14 publications

Intelligent Mechanisms for PdM in Automotive Machinery: A Comprehensive Analysis using ML/DL

Intelligent Mechanisms for PdM in Automotive Machinery: A Comprehensive Analysis using ML/DL

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

On the Challenges of Applying Machine Learning in Mineral Processing and Extractive Metallurgy

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