predictive maintenance
117533006
A new dynamic predictive maintenance framework using deep learning for failure prognostics
In Prognostic Health and Management (PHM) literature, the predictive maintenance studies can be classified into two groups. The first group focuses on the prognostics step but does not consider the maintenance decisions. The second group addresses the maintenance optimization question based on the assumptions that the prognostics information or the degradation models of the system are already known. However, none of the two groups provides a complete framework (from data driven prognostics to maintenance decisions) investigating the impact of the imperfect prognostics on maintenance decision. Therefore, this paper aims to fill this gap of literature. It presents a novel dynamic predicive maintenance framework based on sensor measurements. In this framework, the prognostics step, based on the Long Short Term Memory network, is oriented towards the requirements of operation planners. It provides the probabilities that the system can fail in different time horizons to decide the moment for preparing and performing maintenance activities. The proposed framework is validated on a real application case study. Its performance is highlighted when compared with two benchmark maintenance policies: classical periodic and ideal predicted maintenance. In addition, the impact of the imperfect prognostics information on maintenance decisions is discussed in this paper.
Reliab. Eng. Syst. Saf.
Khanh T P Nguyen Kamal Medjaher
67405803
Predictive maintenance: strategic use of IT in manufacturing organizations
A combination of big data and predictive analytics orchestrated through the Internet of Things (IoT) offers many opportunities for researchers in Information Systems, Operations Management and Strategy to look at old problems in new ways, and to identify completely new research areas. While there is much hype, little research has been conducted that informs companies about how to profitably integrate the IoT with strategic or operational processes. This paper views the IoT through the lens of predictive maintenance the use of real time data and predictive analytics algorithms to dynamically manage preventive maintenance policies. These are being used by numerous manufacturing organizations to transition from product oriented to service oriented business models. In particular, we analyze optimal preventive maintenance policies in an environment where equipment is subject to a deterioration, which shifts it from its initial, fully productive state, having a specified, age dependent failure rate to a less productive or deteriorated state, having a different, presumably higher, age dependent failure rate. The deterioration, itself, is a random process.
Inf. Syst. Frontiers
Salvatore T March Gary D Scudder
199541019
Sensing and Quality Monitoring Facilities Designed for Pasta Industry Including Traceability, Image Vision and Predictive Maintenance
This paper analyzes different facilities useful for pasta industry. These proposed topics are studied during the development of a research industry project involving the design of tools suitable for production sensing and actuation processes allowing automation. After a discussion about architecture design containing facilities, the attention has been focused on production traceability, image vision quality inspection and predictive maintenance. The predictive maintenance has been implemented by an artificial neural network algorithm able to predict humidity in a controlled environment. The proposed model is suitable for Industry 4.0 implementing internet of things IoT devices.
2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)
Alessandro Massaro I Manfredonia Angelo Maurizio Galiano Leonardo Pellicani Vitangelo Birardi
37287290
Forecasting fault events for predictive maintenance using data driven techniques and ARMA modeling
Abstract Presently, time based airline maintenance scheduling does not take fault predictions into account, but happens at fixed time intervals. This may result in unnecessary maintenance interventions and also in situations where components are not taken out of service despite exceeding their designed risk of failure. To address this issue we propose a framework that can predict when a component/system will be at risk of failure in the future, and therefore, advise when maintenance actions should be taken. In order to facilitate such prediction, we employ an auto regressive moving average (ARMA) model along with data driven techniques, and compare the performance of multiple data driven techniques. The ARMA model adds a new feature that is used within the data driven model to give the final prediction. The novelty of our work is the integration of the ARMA methodology with data driven techniques to predict fault events. This study reports on a real industrial case of unscheduled removals of a critical valve of the aircraft engine. Our results suggest that the support vector regression model can outperform the life usage model on the evaluation measures of sample standard deviation, median error, median absolute error, and percentage error. The generalized linear model provides an effective approach for predictive maintenance with comparable results to the baseline. The remaining data driven models have a lower overall performance.
Comput. Ind. Eng.
Marcia Baptista Shankar Sankararaman Ivo Paixao de Medeiros Cairo L Nascimento Helmut Prendinger Elsa M P Henriques
114284502
Cloud enhanced predictive maintenance
Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition based maintenance decision making through a cloud based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.
Bernard Schmidt Lihui Wang
46966377
Single machine based joint optimization of predictive maintenance planning and production scheduling
Abstract Maintenance planning and production scheduling are two activities that are inter dependent but most often performed independently in manufacturing. The maintenance planning affects both available production time and failure probability. However, in previous research, the maintenance planning only considers preventive maintenance and may result in maintenance shortage or overage. And the deterioration and health status of machines from prognostics are often ignored. The paper presents an integrated decision model that coordinates predictive maintenance decisions based on prognostics information with a single machine scheduling decisions so that the total expected cost is minimized. In the integrated model, the health status and dummy age subjected to machine degradation is considered. Finally, a case study is used to demonstrate the value of the proposed methods. And the performance of the integrated solution is compared with solutions obtained from solving the predictive maintenance planning and production scheduling problems independently. The results prove its efficiency.
Qinming Liu Ming Dong F Frank Chen Wenyuan Lv Chunming Ye
67244234
The role of data fusion in predictive maintenance using digital twin
Modern aerospace industry is migrating from reactive to proactive and predictive maintenance to increase platform operational availability and efficiency, extend its useful life cycle and reduce its life cycle cost. Multiphysics modeling together with data driven analytics generate a new paradigm called "Digital Twin." The digital twin is actually a living model of the physical asset or system, which continually adapts to operational changes based on the collected online data and information, and can forecast the future of the corresponding physical counterpart. This paper reviews the overall framework to develop a digital twin coupled with the industrial Internet of Things technology to advance aerospace platforms autonomy. Data fusion techniques particularly play a significant role in the digital twin framework. The flow of information from raw data to high level decision making is propelled by sensor to sensor, sensor to model, and model to model fusion. This paper further discusses and identifies the role of data fusion in the digital twin framework for aircraft predictive maintenance.Modern aerospace industry is migrating from reactive to proactive and predictive maintenance to increase platform operational availability and efficiency, extend its useful life cycle and reduce its life cycle cost. Multiphysics modeling together with data driven analytics generate a new paradigm called "Digital Twin." The digital twin is actually a living model of the physical asset or system, which continually adapts to operational changes based on the collected online data and information, and can forecast the future of the corresponding physical counterpart. This paper reviews the overall framework to develop a digital twin coupled with the industrial Internet of Things technology to advance aerospace platforms autonomy. Data fusion techniques particularly play a significant role in the digital twin framework. The flow of information from raw data to high level decision making is propelled by sensor to sensor, sensor to model, and model to model fusion. This paper further discusses and identifies the
Zheng Liu Norbert G H Meyendorf Nezih Mrad
49272171
A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance
The area of predictive maintenance has taken a lot of prominence in the last couple of years due to various reasons. With new algorithms and methodologies growing across different learning methods, it has remained a challenge for industries to adopt which method is fit, robust and provide most accurate detection. Fault detection is one of the critical components of predictive maintenance; it is very much needed for industries to detect faults early and accurately. In a production environment, to minimize the cost of maintenance, sometimes it is required to build a model with minimal or no historical data. In such cases, unsupervised learning would be a better option model building. In this paper, we have chosen a simple vibration data collected from an exhaust fan, and have fit different unsupervised learning algorithms such as PCA T2 statistic, Hierarchical clustering, K Means, Fuzzy C Means clustering and model based clustering to test its accuracy, performance, and robustness. In the end, we have proposed a methodology to benchmark different algorithms and choosing the final model.
2018 5th International Conference on Industrial Engineering and Applications (ICIEA)
Nagdev Amruthnath Tarun Gupta
52148890
Machine Learning approach for Predictive Maintenance in Industry 4.0
Condition monitoring together with predictive maintenance of electric motors and other equipment used by the industry avoids severe economic losses resulting from unexpected motor failures and greatly improves the system reliability. This paper describes a Machine Learning architecture for Predictive Maintenance, based on Random Forest approach. The system was tested on a real industry example, by developing the data collection and data system analysis, applying the Machine Learning approach and comparing it to the simulation tool analysis. Data has been collected by various sensors, machine PLCs and communication protocols and made available to Data Analysis Tool on the Azure Cloud architecture. Preliminary results show a proper behavior of the approach on predicting different machine states with high accuracy.
2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)
Marina Paolanti L Romeo Andrea Felicetti Adriano Mancini E Frontoni Jelena Loncarski
49679953
Anomaly detection and predictive maintenance for photovoltaic systems
Abstract We present a learning approach designed to detect possible anomalies in photovoltaic (PV) systems in order to let an operator to plan predictive maintenance interventions. The anomaly detection algorithm presented is based on the comparison between the measured and the predicted values of the AC power production. The model designed to predict the AC power production is based on an Artificial Neural Network (ANN) that is capable of estimating the AC power production using solar irradiance and PV panel temperature measurements, and that is trained using a dataset previously gathered from the plant to be monitored. Live trend data coming from the PV system are then compared with the output of the model and the vector of residuals is analyzed to detect anomalies and generate daily predictive maintenance alerts; there residuals are aggregated over 1 day and processed to detect out of threshold samples and system degradation trends; these trends are extracted by computing the Triangular Moving Average (TMA) where the window size is automatically determined. The paper also reports experimental data results revealing that the model leads to a good anomaly detection rate, which is measured as a positive predictive detection rate greater than 90% Moreover, the algorithm is able to recognize trends of system's deviations from normal operation behavior and generate predictive maintenance alerts as a decision support system for operatives, with the aim of avoiding possible incoming failures.
Neurocomputing
Massimiliano De Benedetti Fabio Leonardi Fabrizio Messina Corrado Santoro Athanasios V Vasilakos