Neural Network Prognostics Model for Industrial Equipment Maintenance

This paper presents a new prognostics model based on neural network technique for supporting industrial maintenance decision. In this study, the probabilities of failure based on the real condition equipment are initially calculated by using logistic regression method. The failure probabilities are subsequently utilized as input for prognostics model to predict the future value of failure condition and then used to estimate remaining useful lifetime of equipment. By having a time series of predicted failure probability, the failure distribution can be generated and used in the maintenance cost model to decide the optimal time to do maintenance. The proposed prognostic model is implemented in the industrial equipment known as autoclave burner. The result from the model reveals that it can give prior warnings and indication to the maintenance department to take an appropriate decision instead of dealing with the failures while the autoclave burner is still operating. This significant contribution provides new insights into the maintenance strategy which enables the use of existing condition data from industrial equipment and prognostics approach

Neural Network Prognostics Model For Industrial Equipment Maintenance

This paper presents a new prognostics model based on neural network technique for supporting industrial maintenance decision. In this study, the probabilities of failure based on the real condition equipment are initially calculated by using logistic regression method. The failure probabilities are subsequently utilized as input for prognostics model to predict the future value of failure condition and then used to estimate remaining useful lifetime of equipment, by having a time series of predicted failure probability, the failure distribution can be generated and used in the maintenance cost model to decide the optimal time to do maintenance. The proposed prognostic model is implemented in the industrial equipment known as autoclave burner. The result from the model reveals that it can give prior warnings and indication to the maintenance department to take an appropriate decision instead of dealing with the failures while the autoclave burner is still operating. This significant contribution provides new insights into the maintenance strategy which enables the use of existing condition data from industrial equipment and prognostics approac

A data-driven prognostic model using time series prediction techniques in maintenance decision making

In recent years, current maintenance strategies have extensively evolved in condition-based maintenance solution in order to achieve a near-zero downtime of equipment function. One of these support elements is the use of prognostic. Prognostic has progressed as a specific function over for the last few years. It provides failure prediction and remaining useful lifetime (RUL) estimation of a targeted equipment or component. This estimation is beneficial for production or maintenance people as it allows them to focus on proactive rather than reactive action. While some prognostic models are created based on the historical failure data, others remain as simulation models serving as a pre-exposure effect analysis. Although the concept of a data-driven prognostics model using condition monitoring information has been widely proposed, the validation in predicting the target value continues to be a challenge. In addition, the prognostics have not been applied directly within the maintenance decision making. Hence, the aim of this study is to design a data driven prognostics model that predicts the series of future equipment condition iteratively and allows the process of maintenance decision making to be carried out. The initial phase of this research deals with a conceptual design of data-driven prognostics model. This conceptual design leads to the formulation of a generic data acquisition and time series prediction techniques, which are the key elements to predictive prognostic solution. In this case, there are four techniques have been used and formulated to have better prognostic results namely: Double Exponential Smoothing (DES), Neural Network (NN), Hybrid DES-NN and Enhanced Double Exponential Smoothing (EDES). The intermediate phase of this research involves the development of a computational tool based on the proposed conceptual model. This tool is used for model implementation that uses the experimental data to test the ability of the prognostics model for failure prediction and RUL estimation. It also demonstrates the integration of prognostics model in maintenance decision making. The final phase of this research demonstrates the implementation of the model using industry data. In this phase, the industrial implementation takes into account the performance accuracy to verify the operational framework. The results from the model implementations have shown that the proposed prognostic model can generate the degradation index from the data acquisition, and the formulated EDES can predict RUL estimation consistently. By integrating it with the maintenance cost model, the proposed prognostic model also can perform time–to-maintenance decision. However, the accuracy of the prognostic and maintenance results can be increased with a huge and quality data. In conclusion, this research contributes to the development of data-driven prognostics model based on condition monitoring information using time series prediction techniques to support maintenance decision

Failure analysis informing intelligent asset management

With increasing demands on the UK’s power grid it has become increasingly important to reform the methods of asset management used to maintain it. The science of Prognostics and Health Management (PHM) presents interesting possibilities by allowing the online diagnosis of faults in a component and the dynamic trending of its remaining useful life (RUL). Before a PHM system can be developed an extensive failure analysis must be conducted on the asset in question to determine the mechanisms of failure and their associated data precursors that precede them. In order to gain experience in the development of prognostic systems we have conducted a study of commercial power relays, using a data capture regime that revealed precursors to relay failure. We were able to determine important failure precursors for both stuck open failures caused by contact erosion and stuck closed failures caused by material transfer and are in a position to develop a more detailed prognostic system from this base. This research when expanded and applied to a system such as the power grid, presents an opportunity for more efficient asset management when compared to maintenance based upon time to replacement or purely on condition

A framework for developing a prognostic model using partial discharge data from electrical trees

Insulation breakdown is a key failure mode of high voltage (HV) equipment, with progressive faults such as electrical treeing leading to potentially catastrophic failure. Electrical treeing proceeds from defects in solid insulation, and cables are particularly affected. Research has shown that diagnosis of the fault can be achieved based on partial discharge (PD) analysis. Nonetheless, after diagnosis of a defect, engineers need to know how long they have to take action. This requires prognosis of remaining insulation life. The progression of a defect is far less well understood than diagnosis, making prognosis a key challenge requiring new approaches to defect modelling. The practical deployment of prognostics for cable monitoring is not currently feasible, due to the lack of understanding of degradation mechanisms and limited data relating defect inception to plant failure. However, this thesis advances the academic state of the art, with an eye towards practical deployment in the future. The expected beneficiaries of this work are therefore researchers in the field of HV condition monitoring in general, and electrical treeing within cables in particular. This research work develops a prognostic model of insulation failure due to the electrical treeing phenomenon by utilising the associated PD data from previous experiment. Both phase-resolved and pulse sequence approaches were employed for PD features extraction. The performance of the PD features as prognostic parameters were evaluated using three metrics, monotonicity, prognosability and trendability. The analysis revealed that features from pulse sequence approach are better than phase-resolved approach in terms of monotonicity and prognosability. The key contributions to knowledge of this work are three-fold: the selection of the most appropriate prognostic parameter for PD in electrical trees, through thorough analysis of the behaviour of a number of candidate parameters; a prognostic modelling approach for this parameter based on curve-fitting; and a generalised framework for prognostic modelling using data-driven techniques.Insulation breakdown is a key failure mode of high voltage (HV) equipment, with progressive faults such as electrical treeing leading to potentially catastrophic failure. Electrical treeing proceeds from defects in solid insulation, and cables are particularly affected. Research has shown that diagnosis of the fault can be achieved based on partial discharge (PD) analysis. Nonetheless, after diagnosis of a defect, engineers need to know how long they have to take action. This requires prognosis of remaining insulation life. The progression of a defect is far less well understood than diagnosis, making prognosis a key challenge requiring new approaches to defect modelling. The practical deployment of prognostics for cable monitoring is not currently feasible, due to the lack of understanding of degradation mechanisms and limited data relating defect inception to plant failure. However, this thesis advances the academic state of the art, with an eye towards practical deployment in the future. The expected beneficiaries of this work are therefore researchers in the field of HV condition monitoring in general, and electrical treeing within cables in particular. This research work develops a prognostic model of insulation failure due to the electrical treeing phenomenon by utilising the associated PD data from previous experiment. Both phase-resolved and pulse sequence approaches were employed for PD features extraction. The performance of the PD features as prognostic parameters were evaluated using three metrics, monotonicity, prognosability and trendability. The analysis revealed that features from pulse sequence approach are better than phase-resolved approach in terms of monotonicity and prognosability. The key contributions to knowledge of this work are three-fold: the selection of the most appropriate prognostic parameter for PD in electrical trees, through thorough analysis of the behaviour of a number of candidate parameters; a prognostic modelling approach for this parameter based on curve-fitting; and a generalised framework for prognostic modelling using data-driven techniques

Η ανάπτυξη διαγνωστικής και προγνωστικής φιλοσοφίας συντήρησης σύνθετων συστημάτων με την αξιοποίηση της Τεχνολογίας Πληροφορικής και Επικοινωνιών και μεθόδων μηχανικής μάθησης.

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Τεχνο-Οικονομικά Συστήματα (ΜΒΑ)