An Industrial Case Study Using Vibration Data and Machine Learning to Predict Asset Health

Amihai, Ido; Gitzel, Ralf; Kotriwala, Arzam; Pareschi, Diego; Subbiah, Subanataranjan; Sosale, Guruprasad

doi:10.1109/cbi.2018.00028

Cited by 42 publications

(36 citation statements)

References 9 publications

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“…Some authors [10,30,[46][47][48][75][76][77]107,114,[118][119][120][121] focused on ANN ML algorithms. Some other authors [64][65][66][67]81,82,[84][85][86][87][88]90,116,117] studied RF technique. In the last years, use of SVM technique has received attention from authors [14,21,48,56,66,79,[93][94][95][96][97][98][99][100]102,107,111,115,121,124].…”

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confidence: 99%

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

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confidence: 99%

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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“…ANNs were employed in studies of engines to analyse vibrations and predict equipment failure [2,4,7,22,40,41,42]. A study by Plante, Nejadpak and Yang [7] showed that by observing the vibration of motors it was possible to identify those considered normal in the operation of the machinery and those related to failure.…”

Section: Related Workmentioning

confidence: 99%

“…The control, monitoring and maintenance of production line equipment are fundamental activities for the quality and performance of the productive process [1,2,3,4]. Sensors and actuators play an important role in the operation of various machines such as conveyor belts, generators, mixers, compressors, furnaces, welding machines, among others, so they must always be in proper working condition.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid these two types of maintenance, industry has begun to perform condition-based maintenance where predictive equipment status is used to plan a maintenance. Employing this method has become part of the smart industrial maintenance and results in fewer downtimes than scheduled maintenance, as it avoids unnecessary maintenance, and reduces corrective maintenance by anticipating possible equipment failures [2,4,7,8,9,10,11]. Machine learning techniques such as Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF) and Support Vector Machine (SVM) are being used to perform regression and prediction tasks in various applications [10,12,13,14,15,16,17,18,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Motor Failure Time Using An Artificial Neural Network

Sampaio

Filho

Silva³

et al. 2019

Sensors

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Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.

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“…As an example, temperature monitoring via infrared can provide data for early failure detection cost-efficiently [16]. For the data analysis, a defragmented infrastructure for big data analysis and the use of artificial intelligence (AI) methods [17] help to simplify and initiate decision support from complex industrial data sets [18][19][20]. Combining methods of industrial AI with novel sensing technology enables new economical, technical solutions, such as condition monitoring and predictive maintenance ( Figure 2).…”

Section: Introductionmentioning

confidence: 99%

Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

Hoffmann¹,

Wildermuth²,

Gitzel³

et al. 2020

Sensors

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The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.

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An Industrial Case Study Using Vibration Data and Machine Learning to Predict Asset Health

Cited by 42 publications

References 9 publications

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Prediction of Motor Failure Time Using An Artificial Neural Network

Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

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