لباس ماشین ابزار CNC در پیش آگهی با استفاده از شبکه های بیزی دینامیکی

The 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.

The maintenance activity plays a major role in industrial systems as it permits to improve the availability, reliability and security, while reducing the life cycle cost. There exist several types of maintenance, which can be classified into two main categories, namely: curative and preventive maintenances [1] and [2]. In the first case, the interventions are done only after the observation of the failure, whereas in the second case they are realized either systematically or conditionally to the health condition of the system. This type of maintenance is commonly termed as a condition based maintenance (CBM). Indeed, the condition of the industrial system is continuously monitored and inspected by a set of sensors. The data recorded by these latter are then processed in order to extract relevant features that allow to estimate the current health state and to project this one in the future. The estimated and projected states are then used to take appropriate maintenance decisions. Diagnostic aims at assessing the component's current condition and identifying the cause of its failure, whereas prognostic is used to predict its future health state in order to anticipate the failure [3], [4], [5] and [6]. Formally, failure prognostic consists of estimating the time before failure or the remaining useful life (RUL) and the associated confidence value. It can be realized by using three main approaches [7] and [8], namely: model-based prognostic, experience-based prognostic and data-driven prognostic. Model-based prognostic consists of studying each component or sub-system in order to establish for each one of them a mathematical model of the degradation phenomenon. The derived model is then used to predict the future evolution of the degradation and thus the related RUL value. Experience-based prognostic methods use mainly probabilistic or stochastic models of the degradation phenomenon, or of the life cycle of the components, by taking into account the data and knowledge accumulated by experience during the whole exploitation period of the industrial system. Data-driven prognostic is based on the transformation of the monitoring data into relevant behavioral models permitting to predict the RUL and the associated confidence. This paper deals with the assessment of the cutting tool's health condition of computer numerical control (CNC) machines through the utilization of dynamic Bayesian networks (DBN). The proposed method belongs to the data-driven prognostic approach and aims at transforming the raw monitoring data provided by the sensors into behavioral models that represent the evolution of the cutting tool's degradation. The obtained models are then used to continuously estimate the current state of the cutting tool and calculate its RUL. The choice of this approach dwells in the fact that in the assessment of the cutting tool's condition of CNC machines, the main problem is that deriving a behavioral model in an analytical form that best fits the dynamic of the tool's wear is not a trivial task. Furthermore, finding experience data for a long period of time is expensive and not easy in practice. This is why the utilization of the data provided by the monitoring sensors may be a trade-off between the model-based prognostic and the experience-based prognostic. Thus, the idea behind this contribution is the transformation of the raw monitoring data into relevant models representing the wear's behavior of the cutting tools of CNC machines. The proposed method relies on two main phases: a learning phase and an assessment phase, as this is done in the framework of data-driven system health monitoring and prognostic [9] and [10]. During the first phase, the raw data are used to extract reliable features, which are then used to learn behavioral models representing the dynamic of the degradation in the cutting tool. The modeling of the degradation is done by using a mixture of Gaussians Hidden Markov Model (MoG-HMM) represented by a DBN. This probabilistic graphical model allows to use continuous observations and also to speed up the inference by using the algorithms proposed for DBNs [11]. In the second phase, the learned models are exploited on line to assess the current health state of the cutting tool and to estimate the value of the RUL and its associated confidence value. The paper is organized as follows: in Section 2 the diagnostic and prognostic paradigms are presented, where some definitions and the related state of the art are given, Section 3 is dedicated to the proposed diagnostic and prognostic method and finally, an application example and simulation results are given in Section 4.

A condition monitoring, diagnostic and prognostic data-driven method has been proposed in this paper. The main idea of the work relies on the transformation of the monitoring data into relevant models that capture the degradation's behavior of the machining tools. The choice of a data-driven method instead of a physical model of the degradation dwells in the fact that obtaining this latter may not a trivial task due to the complexity of the physical phenomenon of the wear, which is not easy to model. The utilization of MoG-HMMs allowed thus to model the wear in a stochastic way by taking into account the different stages of the degradation. The identification of the current condition of the tool combined with the models learned in the off-line phase allowed to calculate the RUL and the associated confidence value. Future works concern the extension of the failure prognostic method by taking into account the variable operating conditions (load, velocity, temperature, etc.) and the integration of maintenance actions for RUL prediction.