Structural reliability prediction of a steel bridge element using dynamic object oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method

A Framework of an Intelligent Maintenance Prognosis Tool

The technology of prognosis has become a significant approach but its implementation in maintenance has a major extension. The ability prognosis in the medical area has been established to estimate the future of human health. However, in maintenance, application of prognosis is not yet seen as a practical use for making better maintenance decision. To date, research in this area has been done in proposing prognosis techniques or model but leaving the implementation of prognosis as their future work. In this paper, an overview of prognosis in maintenance is presented. By using the data-driven approach, a framework for implementing of an intelligent maintenance prognosis tool is introduced. The framework utilizes the existing equipment operating performance data in the industry for prognosis process. Next, the framework combines the ability of prognosis in estimating remaining useful life (RUL) of equipment with the maintenance action knowledge to generate a well-received maintenance plan

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

Methodology for assessing system performance loss within a proactive maintenance framework

Maintenance plays now a critical role in manufacturing for achieving important cost savings and competitive advantage while preserving product conditions. It suggests moving from conventional maintenance practices to predictive strategy. Indeed the maintenance action has to be done at the right time based on the system performance and component Remaining Useful Life (RUL) assessed by a prognostic process. In that way, this paper proposes a methodology in order to evaluate the performance loss of the system according to the degradation of component and the deviations of system input flows. This methodology is supported by the neuro-fuzzy tool ANFIS (Adaptive Neuro-Fuzzy Inference Systems) that allows to integrate knowledge from two different sources: expertise and real data. The feasibility and added value of such methodology is then highlighted through an application case extracted from the TELMA platform used for education and research

A Multiclassifier Approach for Drill Wear Prediction

Classification methods have been widely used during last years in order to predict patterns and trends of interest in data. In present paper, a multiclassifier approach that combines the output of some of the most popular data mining algorithms is shown. The approach is based on voting criteria, by estimating the confidence distributions of each algorithm individually and combining them according to three different methods: confidence voting, weighted voting and majority voting. To illustrate its applicability in a real problem, the drill wear detection in machine-tool sector is addressed. In this study, the accuracy obtained by each isolated classifier is compared with the performance of the multiclassifier when characterizing the patterns of interest involved in the drilling process and predicting the drill wear. Experimental results show that, in general, false positives obtained by the classifiers can be slightly reduced by using the multiclassifier approach

Combining a recurrent neural network and a PID controller for prognostic purpose.

International audienceIn maintenance field, prognostic is recognized as a key feature as the prediction of the remaining useful life of a system allows avoiding inopportune maintenance spending. Assuming that it can be difficult to provide models for that purpose, artificial neural networks appear to be well suited. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and a proportional integral derivative controller (PID) is proposed in order to improve the accuracy of predictions. The PID controller attempts to correct the error between the real process variable and the neural network predictions. The approach and its performances are illustrated by using two classical prediction benchmarks: the Mackey–Glass chaotic time series and the Box–Jenkins furnace data

Dynamic reliability analysis of corroded pipeline using Bayesian Network

Funding Information: The authors would like to express appreciation for the support of the sponsor Universiti Malaysia Pahang Internal Grant [RDU1703169].Peer reviewedPublisher PD

Hidden Markov models for failure diagnostic and prognostic.

International audienceThis paper deals with an estimation of the Remaining Useful Life of bearings based on the utilization of Mixture of Gaussians Hidden Markov Models (MoG-HMMs). The raw signals provided by the sensors are first processed to extract features, which permit to model the physical component and its degradation. The prognostic process is done in two phases: a learning phase and an evaluation phase. During the first phase, the sensors' data are processed in order to extract appropriate and useful features, which are then used as inputs of dedicated learning algorithms in order to estimate the parameters of a MoG-HMM. The obtained model represents the behavior of the component including its degradation. In addition, the model contains the number of health states and the stay durations in each state. Once the learning phase is done, the generated model is exploited during the second phase, where the extracted features are continuously injected to the learned model to assess the current health state of the physical component and to estimate its remaining useful life and the associated confidence. The proposed method is tested on a benchmark data taken from the "NASA prognostic data repository" related to bearings used under several operating conditions. Moreover, the developed method is compared to two methods: the first using traditional HMMs with exponential time durations and the second using regular Hidden Semi Markov Model (HSMM). Finally, simulation results are given and discussed at the end of the paper

Maintain maintenance: a look at some threats in the sector

[EN] Industrial maintenance is a key factor in ensuring the availability of the production systems of companies. Furthermore, it also has a significant impact on energy efficiency and on safety. The study of the status of the maintenance departments activity in the industrial sector in Spain (and probably in other European countries) shows negative aspects that indicate little innovation, lack of resources or personnel, poor planning and a downward trend in own staffing levels, while hiring with outside companies increases. Many companies perceive maintenance as an unavoidable cost. To change these negative trends it is necessary to make visible to the management staff the cost-benefit analysis of the maintenance activity and justify the investments to improve their results. This article highlights some of the threats that affect the activity of maintenance departments in industry, as perceived through statistics on industrial activity and sectoral surveys in Spain. Secondly, the article proposes the basis of a cost-benefit analysis, based on the avoided costs that maintenance department activity can generate. The authors propose this model as a simple tool to justify investments in the maintenance departments of companies.Roldán-Porta, C.; Cárcel Carrasco, FJ.; Escrivá-Escrivá, G.; Roldán-Blay, C. (2014). Maintain maintenance: a look at some threats in the sector. International Journal of Services Technology and Management. 20(6):233-250. doi:10.1504/IJSTM.2014.068856S23325020

A neuro-fuzzy self built system for prognostics : a way to ensure good prediction accuracy by balancing complexity and generalization.

International audienceIn maintenance field, prognostics is recognized as a key feature as the prediction of the remaining useful life of a system allows avoiding inopportune maintenance spending. However, it can be a non trivial task to develop and implement effective prognostics models including the inherent uncertainty of prognostics. Moreover, there is no systematic way to construct a prognostics tool since the user can make some assumptions: choice of a structure, initialization of parameters... This last problem is addressed in the paper: how to build a prognostics system with no human intervention, neither a priori knowledge? The proposition is based on the use of a neuro-fuzzy predictor whose architecture is partially determined thanks to a statistical approach based on the Akaike information criterion. It consists in using a cost function in the learning phase in order to automatically generate an accurate prediction system that reaches a compromise between complexity and generalization capability. The proposition is illustrated and discussed