On-condition maintenance of diesel engines modelled by a hidden Markov model (original) (raw)
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The State of the Art of Hidden Markov Models for Predictive Maintenance of Diesel Engines
Quality and Reliability Engineering International, 2017
The maintenance of diesel Engines is usually scheduled according to the maintenance procedures defined by manufacturers. However, the state of the art shows that the condition monitoring maintenance associated with adequate prediction algorithms allows performance improvement both by increasing the intervals between interventions and by helping to maintain reliability levels. There are many types of variables that can be used to measure equipment condition, as is the case of several types of pollutant emissions such as NO x , CO2, HC, PM, and NOISE, among others. This is a typical problem that can be solved through a hidden Markov model, taking into account the specificity of this type of equipment. The paper describes two algorithms that can help to increase the quality of assessment of engine states and the efficiency of maintenance planning. Those are the Viterbi and Baum-Welch algorithms. The importance of how to calculate the performance index of the model by the use of the perplexity algorithm is also emphasized. In this paper, a new paradigm is proposed, designated as ecological predictive maintenance.
Ecological Predictive Maintenance of Diesel Engines
Diesel and Gasoline Engines, 2020
The ecological predictive maintenance (EPM) of diesel engines is a great contribution to improve the environment and to stimulate good practices with good impact in the human health. The ecology is a rapidly developing scientific discipline with great relevance to a sustainable world, whose development is not complete as a mature theory. There are, however, general principles emerging that may facilitate the development of such theory. In the meantime, these principles can serve as useful guides for EPM. According to the state of the art, it can be stated that through prediction algorithms, the equipment's performance can be improved. To support this approach, it is necessary to implement a good condition monitoring maintenance. The result permits to maximise the time spacing between interventions and to increase the reliability levels. The condition variables of each equipment can be monitored according to their specificity, such as temperature, humidity, pollutant emissions (NO x ,CO 2 , HC and PM), emitted noise, etc. The environment where the equipment is inserted also must be considered. The assessment of the equipment's condition can be done by Hidden Markov Models (HMM), namely diesel engines. This chapter presents two algorithms-Viterbi and Baum-Welch algorithms-that, through the prediction of the equipment's condition, help to increase the efficiency of the maintenance planning.
Hidden Markov Model for Health Estimation and Prognosis of Turbofan Engines
Volume 3: 2011 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts A and B, 2011
Determining the residual life time of systems is a determinant factor for machinery and environment safety. In this paper the problem of estimate the residual useful life (RUL) of turbofan engines is addressed. The adopted approach is especially suitable for situations in which a large amount of data is available offline, by allowing the processing of such data for the determination of RUL. The procedure allows to calculate the RUL through the following steps: features extraction by Artificial Neural Networks (ANN) and determination of remaining life time by prediction models based on a Hidden Markov Model (HMM). Simulations confirm the effectiveness of the proposed approach and the promising power of Bayesian methods.
Condition-based maintenance strategy for vehicles using hidden Markov models
Advances in Mechanical Engineering
Operation and maintenance have their own impact in every field. Maintenance strategy is followed to provide unwavering quality and security for a healthy transportation system. Therefore, the transportation system requires an appropriate maintenance schedule of the vehicles. The classical analysis of the present and future performance of systems tries to assure that the safety and operational condition of the system so as to enhance the ability of credentials of proactive malfunction circumstances. Condition-based maintenance identifies the vehicle status based on wire or wireless monitored data and predicts malfunction to carry out suitable maintenance actions like repair and replacement before it happens. Different uncertainties like terrain, mileage of the vehicle and applied load on the vehicles have been utilized as the constraints of fuzzy-based vehicle maintenance scheduling. The response of vehicle maintenance scheduling (VMS) provides the details regarding the type of maint...
Hidden Semi-Markov Models for Predictive Maintenance
Mathematical Problems in Engineering, 2015
Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.
Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study
Applied Sciences, 2021
The availability maximization is a goal for any organization because the equipment downtime implies high non-production costs and, additionally, the abnormal stopping and restarting usually imply loss of product’s quality. In this way, a method for predicting the equipment’s health state is vital to maintain the production flow as well as to plan maintenance intervention strategies. This paper presents a maintenance prediction approach based on sensing data managed by Hidden Markov Models (HMM). To do so, a diagnosis of drying presses in a pulp industry is used as case study, which is done based on data collected every minute for three years and ten months. This paper presents an approach to manage a multivariate analysis, in this case merging the values of sensors, and optimizing the observable states to insert into a HMM model, which permits to identify three hidden states that characterize the equipment’s health state: “Proper Function”, “Alert state”, and “Equipment Failure”. Th...
Annual Conference of the PHM Society, 2019
Remaining Useful Life (RUL) estimation is critical in many engineering systems where proper predictive maintenance is needed to increase a unit's effectiveness and reduce time and cost of repairing. Typically for such systems, multiple sensors are normally used to monitor performance, which create difficulties for system state identification. In this paper, we develop a semi-supervised left-to-right constrained Hidden Markov Model (HMM) model, which is effective in estimating the RUL, while capturing the jumps among states in condition dynamics. In addition, based on the HMM model learned from multiple sensors, we build a Partial Observable Markov Decision Process (POMDP) to demonstrate how such RUL estimation can be effectively used for optimal preventative maintenance decision making. We apply this technique to the NASA Engine degradation data and demonstrate the effectiveness of the proposed method.
Methods to choose the best Hidden Markov Model topology for improving maintenance policy
2012
Prediction of physical particular phenomenon is based on partial knowledge of this phenomenon. Theses knowledges help us to conceptualize this phenomenon according to different models. Hidden Markov Models (HMM) can be used for modeling complex processes. We use this kind of models as tool for fault diagnosis systems. Nowadays, industrial robots living in stochastic environment need faults detection to prevent any breakdown. In this paper, we wish to find the best Hidden Markov Model topologies to be used in predictive maintenance system. To this end, we use a synthetic Hidden Markov Model in order to simulate a real industrial CMMS *. In a stochastic way, we evaluate relevance of Hidden Markov Models parameters, without a priori knowledges. After a brief presentation of a Hidden Markov Model, we present the most used selection criteria of models in current literature. We support our study by an example of simulated industrial process by using our synthetic model. Therefore, we evaluate output parameters of the various tested models on this process: topologies, learning algorithms, observations distributions, epistemic uncertainties. Finally, we come up with the best model which will be used to improve maintenance policy and worker safety.