Christophe Letot

A degradation model for maintenance improvement in respect of cost and availability

Purpose – The purpose of this paper is to highlight the need for degradation data in order to improve the reliability and the mean residual life estimation of a specific item of equipment and to adapt the preventive maintenance tasks accordingly. Design/methodology/approach – An initial reliability model which uses a degradation-based reliability model that is built from the collection of hitting times of a failure threshold. The proposed maintenance model is based on the cost/availability criterion. The estimation of both reliability and optimum time for preventive maintenance are updated with all new degradation data that are collected during operating time. Findings – An improvement for the occurrences of maintenance tasks which minimizes the mean cost per unit of time and increases the availability. Practical implications – Inspection tasks to measure the degradation level should be realized at least one time for each item of equipment at a specific time determined by the proposed methodology. Origina...

Actualized reliability model based on degradation data and maintenance optimization

Maintenance of industrial equipment is a lever to increase the efficiency and the productivity of industries. An intelligent maintenance is achieved by determining the optimum preventive time to replace an item in order to minimize the costs and to increase the availability. In order to reach the optimum, one has to know the reliability and the mean residual lifetime of an item. The reliability R(t) is the probability that an item will perform a required function without failure under stated conditions for a stated period of time. This information may be obtained by three approaches that depend of the available data [1].

Evaluation of the effect of tamping on the track geometry condition: A case study

Tamping is one of the major activities undertaken by railway maintenance managers to recover the track geometry condition. Modelling the effectiveness of tamping along with track geometry degradation is essential for long-term prediction of track geometry behaviour. The aim of this study is to analyse the effect of tamping on the different track geometry measurements, i.e. longitudinal level, alignment and cant, based on inspection car records from a part of the Main Western Line in Sweden. To model recovery after tamping, a probabilistic approach is applied. The track geometry condition before tamping was considered as the dominant factor for modelling the model parameters. Correlation analysis was performed to measure the linear relation between the recoveries of the different geometry measures. The results show a moderate correlation between the recovery of the longitudinal level and that of the cant, and a weak correlation between the recovery of the longitudinal level and that of the alignment. Linear regression and Wiener process were also applied to model track geometry degradation and to obtain degradation rates. The effect of tamping on degradation rate was analysed. It was observed that degradation rate increased after tamping.

Uncertainty Management of Cutting Forces Parameters and its Effects on Machining Stability

Virtual manufacturing is a field of research which numerically simulate all the manufacturing processes seen by a mechanical part during its production (for example casting, forging, machining, heat treatment,…). Its use is rising on various industries to reduce production costs and improve quality of manufactured parts. One of the most challenging component of the whole simulation chain is the simulation of machining operations due to some of its specificities (need of material law at high strain, strain rates and temperature, heterogeneities of machined material, influence of residual stresses,…).In order to circumvent these difficulties, macroscopic models of machining process have been developed in order to compute more global information (cutting forces, stability of the process, tolerance or roughness for example). For this approach, the cutting forces computation is done by using simple analytical law based on mechanistic approach. The parameters of the models have no clear physical meaning (or at least cannot be linked to intrinsic properties of the material to be machined) and are therefore considered constants for a given set of simulations.The aim of this paper is to take into account the uncertainty on the variability of the cutting force signal during machining operation used as input for mechanistic model identification. The variability of the response during a test on fixed conditions (cutting tool, machined material and cutting parameters) is taken into account to develop a model where parameters of the model can evolve during a given operation.The proposed model is then used as an input of a milling operation simulation in order to study its influence on machining stability as compared to a classical approach.

Assessing uncertainty from data collection to maintenance optimization

In this paper, we will propose a framework to perform the optimization of periodical maintenance tasks for a production line, with a specific viewpoint on uncertainty issues from the modelling step to the analysis of numerical results. From a structured log file of operational data, we build a reliability-based model (block diagram) that is used to optimize the parameters of the maintenance policies through Monte Carlo simulations. The model is determined by using every data source available (Computerized Maintenance Management System hierarchy and failure mode classification especially).

An adaptive degradation-based maintenance model taking into account both imperfect adjustments and AGAN replacements

This paper presents an adaptive maintenance model for equipment that can be adjusted (minor preventive maintenance, imperfect state) or replaced (major preventive maintenance, as good as new) at specific scheduled times based on degradation measurements. An initial reliability law that uses a degradation-based model is built from the collection of hitting times of a failure threshold. Inspections are performed to update the reliability, the remaining useful life, and the optimum time for preventive maintenance. The case of both as good as new replacements and imperfect adjustments is considered. The proposed maintenance model is based on the optimization of the long-term expected cost per unit of time. The model is then tested on a numerical case study to assess its effectiveness. This results in an improvement for the occurrences of maintenance tasks that minimizes the mean cost per unit of time as well as an optimized number of adjustments that can be considered before replacing an item. The practical application is a decision aid support to answer the 2 following questions: Should we intervene now or wait for the next inspection? For each intervention, should we adjust or replace the item of equipment? The originality is the presence of 2 criteria that help the maintainer to decide to postpone or not the preventive replacement time depending on the measured degradation and to decide whether the item should be adjusted or replaced.

Early detection of bearing damage by means of decision trees

Abstract This paper presents a procedure for early detection of rolling bearing damages on the basis of vibration measurements. First, an envelope analysis is performed on bandpass filtered signals. For each frequency range, a feature indicator is defined as sum of spectral lines. These features are passed through a principal component model to generate a single variable which allows to track change in the bearing health. Thresholds and rules for early detection are learned thanks to decision trees. Experimental results demonstrate that this procedure enables early detection of bearing defects.