Data-Driven Predictive Maintenance: A Methodology Primer

2021 ◽  
pp. 39-73
Author(s):  
Tania Cerquitelli ◽  
Nikolaos Nikolakis ◽  
Lia Morra ◽  
Andrea Bellagarda ◽  
Matteo Orlando ◽  
...  
Keyword(s):  
Predictive Maintenance ◽  
Data Driven

scholarly journals Anomaly detection and event mining in cold forming manufacturing processes

2020 ◽  
Author(s):  
Diego Nieves Avendano ◽  
Daniel Caljouw ◽  
Dirk Deschrijver ◽  
Sofie Van Hoecke
Keyword(s):  
Anomaly Detection ◽  
Missing Values ◽  
Acoustic Emissions ◽  
Irregular Sampling ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Normal Operation ◽  
Cold Forming ◽  
Event Prediction ◽  
Event Mining

Abstract Predictive maintenance is one of the main goals within the Industry 4.0 trend. Advances in data-driven techniques offer new opportunities in terms of cost reduction, improved quality control, and increased work safety. This work brings data-driven techniques for two predictive maintenance tasks: anomaly detection and event prediction, applied in the real-world use case of a cold forming manufacturing line for consumer lifestyle products by using acoustic emissions sensors in proximity of the dies of the press module. The proposed models are robust and able to cope with problems such as noise, missing values, and irregular sampling. The detected anomalies are investigated by experts and confirmed to correspond to deviations in the normal operation of the machine. Moreover, we are able to find patterns which are related to the events of interest.

scholarly journals Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant

2020 ◽  
Vol 3 (3) ◽  
pp. 34
Author(s):  
Antoni Escobet ◽  
Teresa Escobet ◽  
Joseba Quevedo ◽  
Adoración Molina
Keyword(s):  
Natural Gas ◽  
Data Analytics ◽  
Liquefied Natural Gas ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Sensor Data ◽  
Gas Consumption ◽  
Using Data ◽  
Maintenance Plan ◽  
Remaining Time

This paper proposes a sensor-data-driven prognosis approach for the predictive maintenance of a liquefied natural gas (LNG) satellite plant. By using data analytics of sensors installed in the satellite plants, it is possible to predict the remaining time to refill the tank of the remote plants. In the proposed approach, the first task of data validation and correction is presented in order to transform raw data into reliable validated data. Then, the second task presents two methods for the prognosis of gas consumption in real time and the forecast of remaining time to refill the tank of the plant. The obtained results with real satellite plants showed good performance for direct implementation in a predictive maintenance plan.

scholarly journals A data-driven predictive maintenance strategy based on accurate failure prognostics

2021 ◽  
Vol 23 (2) ◽  
pp. 387-394
Author(s):  
Chuang Chen ◽  
Cunsong Wang ◽  
Ningyun Lu ◽  
Bin Jiang ◽  
Yin Xing
Keyword(s):  
Predictive Maintenance ◽  
Data Driven ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Engineering Systems ◽  
Data Set ◽  
Periodic Maintenance ◽  
Maintenance Decisions ◽  
Aero Engines ◽  
Failure Prognostics

Maintenance is fundamental to ensure the safety, reliability and availability of engineering systems, and predictive maintenance is the leading one in maintenance technology. This paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect time for taking maintenance activities is determined by evaluating the maintenance cost online that has taken into account of the failure prognostic results of performance degradation. The feasibility and effectiveness of the proposed strategy is confirmed using the NASA data set of aero-engines. Results show that the proposed strategy outperforms the two benchmark maintenance strategies: classical periodic maintenance and emerging dynamic predictive maintenance.

scholarly journals Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2425 ◽  
Author(s):  
Jonas Fausing Olesen ◽  
Hamid Reza Shaker
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Health Management ◽  
Thermal Power ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Thermal Power Plants ◽  
Broad Concept ◽  
Automatic Maintenance ◽  
Critical Components

Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential role in various industries and as such clever maintenance can ensure cost reductions and high availability. Prognostics and Health Management, PHM, is the study utilizing data to estimate the current and future conditions of a system. Within the field of PHM, Predictive Maintenance, PdM, has been gaining increased attention. Data-driven models can be built to estimate the remaining-useful-lifetime of complex systems that would be difficult to identify by man. With the increased attention that the Predictive Maintenance field is receiving, review papers become increasingly important to understand what research has been conducted and what challenges need to be addressed. This paper does so by initially conceptualising the PdM field. A structured overview of literature in regard to application within PdM is presented, before delving into the domain of thermal power plants and pump systems. Finally, related challenges and trends will be outlined. This paper finds that a large number of experimental data-driven models have been successfully deployed, but the PdM field would benefit from more industrial case studies. Furthermore, investigations into the scale-ability of models would benefit industries that are looking into large-scale implementations. Here, examining a method for automatic maintenance of the developed model will be of interest. This paper can be used to understand the PdM field as a broad concept but does also provide a niche understanding of the domain in focus.

Predictive Maintenance Information Systems

2020 ◽  
Vol 16 (2) ◽  
pp. 22-37 ◽  
Author(s):  
Michael Möhring ◽  
Rainer Schmidt ◽  
Barbara Keller ◽  
Kurt Sandkuhl ◽  
Alfred Zimmermann
Keyword(s):  
Information Systems ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Service Provisioning ◽  
Process Automation ◽  
Research And Practice ◽  
Maintenance System ◽  
Advanced Analytics

Predictive maintenance has the potential to improve the reliability of production and service provisioning. However, there is little knowledge about the proper implementation of predictive maintenance in research and practice. Therefore, we conducted a multi-case study and investigated underlying conditions and technological aspects for implementing a predictive maintenance system and where it leads to. We found that predictive maintenance initiatives are triggered by severe impacts of failures on revenue and profit. Furthermore, successful predictive maintenance initiatives require that pre-conditions are fulfilled: Data must be available and accessible. Very important is also the support by the management. We identified four factors important for the implementation of predictive maintenance. The integration of data is highly facilitated by Cloud-based mechanisms. The detection of events is enabled by advanced analytics. The execution of predictive maintenance operations is supported by data-driven process automation and visualization.

Data-driven predictive maintenance policy based on multi-objective optimization approaches for the component repairing problem

2020 ◽  
pp. 1-20 ◽  
Author(s):  
Ornella Pisacane ◽  
Domenico Potena ◽  
Sara Antomarioni ◽  
Maurizio Bevilacqua ◽  
Filippo Emanuele Ciarapica ◽  
...  
Keyword(s):  
Predictive Maintenance ◽  
Data Driven ◽  
Multi Objective Optimization ◽  
Maintenance Policy ◽  
Multi Objective
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