Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

This document is the Accepted Manuscript of the following article: Mohammed Chalouli, Nasr-eddine Berrached, and Mouloud Denai, ‘Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction’, Journal of Failure Analysis and Prevention, Vol. 17 (5): 1053-1066, October 2017. Under embargo. Embargo end date: 31 August 2018. The final publication is available at Springer via DOI: https://doi.org/10.1007/s11668-017-0343-y.Finding reliable condition monitoring solutions for large-scale complex systems is currently a major challenge in industrial research. Since fault diagnosis is directly related to the features of a system, there have been many research studies aimed to develop methods for the selection of the relevant features. Moreover, there are no universal features for a particular application domain such as machine diagnosis. For example, in machine bearing fault diagnosis, these features are often selected by an expert or based on previous experience. Thus, for each bearing machine type, the relevant features must be selected. This paper attempts to solve the problem of relevant features identification by building an automatic fault diagnosis process based on relevant feature selection using a data-driven approach. The proposed approach starts with the extraction of the time-domain features from the input signals. Then, a feature reduction algorithm based on cross-correlation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using K-means++ selects the relevant fault features based on the squared Euclidian distance between different health states. Finally, the selected features are used as inputs to a self-organizing map producing our health indicator. The proposed method is tested on roller bearing benchmark datasets.Peer reviewe

Bayesian based fault diagnosis: Application to an electrical motor

to satisfy (e.g. case of systems in design stage or newly put in service). Furthermore, in practice the knowledge one has about the system can be incomplete or uncertain. Thus, the use of a mathematical tool introducing the notion of probability to take into account this uncertainty and/or incompleteness can be a convenient solution. In the present contribution, we have used bayesian networks for the possibilities they offer in modeling of complex and stochastic systems and also for their learning and inference capabilities [5]. Compared to the previous referenced approaches, bayesian networks allow graphical representation of the knowledge under its different types (rules, causal relationships, experts ’ statements, physical laws, etc.). In addition, parameter as well as graphical structure update is easy to perform when using this kind of tool [5]. In this paper, bayesian networks are used to model the knowledge we have about the process and to perform a fault diagnosis. The tool’s qualitative aspect (directed acyclic graph) allows to represent graphically the causal relations between the process variables. The quantitative part of bayesian networks tool consists in determining the a priori and conditional probability tables of each variable in the generated graph. These probabilities can be given by an expert of the process or obtained by a learning method or algorithm from an experimental or experience feedback database. In the literature, many research works have been proposed on bayesian networks but, most of them are focused on learning algorithms that allow to construct the graphical model and estimate the probabilities of each node of the derived graph [6]. In this contribution, the diagnosis task consisted in computing the a posteriori probability of each process component (or node) given a set of new observations (also called evidences). The present paper is organized as follows: the second sechal-00298240

Failure prognostic by using Dynamic Bayesian Networks

Abstract: this paper presents a procedure for failure prognostic by using Dynamic Bayesian Networks (DBNs). The graphical representation of this tool is particularly well suitable for modeling complex systems, with non homogeneous sources of data and knowledge. Moreover, DBNs allow to deal with uncertainty which is an inherent property to any failure prognostic work, especially regarding the estimation of the Remaining Useful Life (RUL) before a failure. The DBN model can be also used to observe the propagation of the effect of any state of the model on the other remaining states. The proposed procedure is applied on a small industrial system to show its feasibility

Remaining Useful Life Estimation of Critical Components With Application to Bearings

Prognostics activity deals with the estimation of the Remaining Useful Life (RUL) of physical systems based on their current health state and their future operating conditions. RUL estimation can be done by using two main approaches, namely model-based and data-driven approaches. The first approach is based on the utilization of physics of failure models of the degradation, while the second approach is based on the transformation of the data provided by the sensors into models that represent the behavior of the degradation. This paper deals with a data-driven prognostics method, where the RUL of the physical system is assessed depending on its critical component. Once the critical component is identified, and the appropriate sensors installed, the data provided by these sensors are exploited to model the degradation’s behavior. For this purpose, Mixture of Gaussians Hidden Markov Models (MoG-HMMs), represented by Dynamic Bayesian Networks (DBNs), are used as a modeling tool. MoG-HMMs allow us to represent the evolution of the component’s health condition by hidden states by using temporal or frequency features extracted from the raw signals provided by the sensors. The prognostics process is then done in two phases: a learning phase to generate the behavior model, and an exploitation phase to estimate the current health state and calculate the RUL. Furthermore, the performance of the proposed method is verified by implementing prognostics performance metrics, such as accuracy, precision, and prediction horizon. Finally, the proposed method is applied to real data corresponding to the accelerated life of bearings, and experimental results are discussed

E-Maintenance for Photovoltaic Power Generation System

AbstractThe high potential recorded by desert of Algeria in terms of sun exposure offers scope to exploit this source of renewable energy to answer the needs for water supply and public lighting in isolated populations. Our objective is to guarantee the reliability of the photovoltaic power generation equipments located in remote and isolated areas, including photovoltaic pumping stations, expected to supply water to local people for human consumption and for livestock from drilling isolated. Thus, we are focused particularly on the reliability of these systems and their maintenance in operational mode.The introduction of the e-maintenance can reduce cost and time management of equipment, and provide easy access to relevant information for the user by freeing up geographical constraints. Our study aims to introduce the techniques proposed by the e-Maintenance, to improve the management of the maintenance process. In this project a cooperative distributed platform of e-maintenance is proposed, including data acquisition systems, control, maintenance management, diagnosis assistance, management of documentation, etc. An e-maintenance platform is a collaboration platform offering a set of software components and software services for maintenance support (integrated and/or distant) all enable maintenance actors to find each other, and the information they need, and to be able to communicate and work together to achieve maintenance process. In this platform, the process of the various modules must be designed following a process of knowledge capitalization and data processing to ensure the diagnosis and prognosis of the pumping system of the Photovoltaic system and thus ensure its reliability. This processing chain will be tested in a first time in a mini-solar station to show the feasibility of the approach