CNC Machine Tool's wear diagnostic and prognostic by using dynamic bayesian networks.

International audienceThe failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a Computer Numerical Control (CNC) tool machine and the estimation of its Remaining Useful Life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper

CNC machine tool health assessment using Dynamic Bayesian Networks.

International audienceThe failure of critical components in physical systems may have negative consequences on the availability, the productivity, their security and on the environment. Thus, the assessment of the critical component's health condition, which can be done in the diagnostic and prognostic framework, should be constantly ensured. In this paper, a contribution on the assessment of the health condition of the cutting tool from a Computer Numerical Control (CNC) machine tool and the prediction of its remaining useful life before its complete failure is addressed. The proposed method is based on the use of monitoring data and relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are then fed as inputs to the learning algorithms in order to generate relevant models that best represent the behavior of the cutting tool. The second phase is an assessment one, which uses the constructed models to identify the current health state and to compute the remaining useful life and the associated confidence value. The method is applied on monitoring data gathered during several cuts of the CNC tool and simulation results are given and discussed

A mixture of gaussians hidden markov model for failure diagnostic and prognostic.

International audienceThis paper deals with a data-driven diagnostic and prognostic method based on a Mixture of Gaussians Hidden Markov Model. The prognostic process of the proposed method is made in two steps. In the first step, which is performed offline, the monitoring data provided by sensors are processed to extract features, which are then used to learn different models that capture the time evolution of the degradation and therefore of the system's health state. In the second step, performed online, the learned models are exploited to do failure diagnostic and prognostic by estimating the asset's current health state, its remaining useful life and the associated confidence degree. The proposed method is tested on a benchmark data related to several bearings and simulation results are given at the end of the paper

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

A data-driven failure prognostics method based on mixture of gaussians hidden markov models

International audienceThis paper addresses a data-driven prognostics method for the estimation of the Remaining Useful Life (RUL) and the associated confidence value of bearings. The proposed method is based on the utilization of the Wavelet Packet Decomposition (WPD) technique, and the Mixture of Gaussians Hidden Markov Models (MoG-HMM). The method relies on two phases: an off-line phase, and an on-line phase. During the first phase, the raw data provided by the sensors are first processed to extract features in the form of WPD coefficients. The extracted features are then fed to dedicated learning algorithms to estimate the parameters of a corresponding MoG-HMM, which best fits the degradation phenomenon. The generated model is exploited during the second phase to continuously assess the current health state of the physical component, and to estimate its RUL value with the associated confidence. The developed method is tested on benchmark data taken from the "NASA prognostics data repository" related to several experiments of failures on bearings done under different operating conditions. Furthermore, the method is compared to traditional time-feature prognostics and simulation results are given at the end of the paper. The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs. Indeed, the RUL and associated confidence value are relevant information which can be used to take appropriate maintenance and exploitation decisions. In practice, this information may help the maintainers to prepare the necessary material and human resources before the occurrence of a failure. Thus, the traditional maintenance policies involving corrective and preventive maintenance can be replaced by condition based maintenance

Estimation of the remaining useful life by using Wavelet Packet Decomposition and HMMs.

International audienceThis paper deals with an estimation of the Remaining Useful Life of bearings based on the utilization of the Wavelet Packet Decomposition (WPD) and the Mixture of Gaussians Hidden Markov Models (MoG-HMM). The raw data provided by the sensors are first processed to extract features by using the wavelet packet decomposition. This latter provides a more flexible way of time-frequency representation and filtering of a signal, by allowing the use of variable sized windows and different detail levels. The extracted features are then fed as inputs of dedicated learning algorithms in order to estimate the parameters of a mixture of Gaussian Hidden Markov Model. Once this learning phase is achieved, the generated model is exploited during a second phase to continuously assess the current health state of the physical component and to estimate its remaining useful life with the associated confidence value. The proposed method is tested on a benchmark data taken from the “NASA prognostic data repository” related to several bearings'. Bearings are chosen because they are the most used and also the most faulty mechanical element in some industrial systems and process. Furthermore, the method is compared to a traditional timefeature prognostic and some simulation results are given at the end of the paper

(2010)" The ISO 13381-1 Standard's Failure Prognostics Process Through an Example

Abstract- Industrial failure prognostics can be considered as the key process of any condition-based maintenance solution. However, contrary to fault diagnostics which is a mature research and industrial work, failure prognostics is a new field for which few applications exist. In the last decade, the interest for this activity has led to some open and industrial standards where the main objective is to provide users with a guidelines allowing them to perform failure prognostics for a large class of industrial systems. However, these standards, rightly, do not emphasize on any particular example to illustrate their content. The present paper aims at explaining the process of failure prognostics, presented in the standard ISO 13381-1, through an electromechanical example. The purpose is to help beginner researchers in the field of industrial failure prognostics to assimilate the main tasks of the process proposed by the standard. The prognostics process is chosen because it represents the key task among the rest of topics proposed and published by the standard. Thus, the comprehension of this part is important to develop prognostics methods and algorithms based on the solid recommendations given by the international organization for standardization. I

RISK MANAGEMENT BASED ON FUZZY LOGIC FOR A FRANCIS TURBINE

To facilitate the maintenance of hydroelectric groups and ensure the production of electrical energy, we defined a decision making tool to help the construction of maintenance plans by integrating the concept of risk for a Francis turbine currently operating in Colombia. As the risks are generally expressed in terms of human knowledge, it seems very interesting to use fuzzy logic to solve this kind of problems. Moreover, it can also be applied in case of detectable failures (via sensors) or undetectable failures. This work has been done in collaboration with EAFIT University in Colombia