Health Assessment and Life Prediction of cutting tools based on support vector regression.

International audienceThe integrity of machining tools is important to maintain a high level of surface quality. The wear of the tool can lead to poor surface quality of the workpiece and even to damage of the machine. Furthermore, in some applications such as aeronautics and precision engineering, it is preferable to change the tool earlier rather than to loose the workpiece because of its high price compared to the tool's one. Thus, to maintain a high quality of the manufactured pieces, it is necessary to assess and predict the level of wear of the cutting tool. This can be done by using condition monitoring and prognostics. The aim is then to estimate and predict the amount of wear and calculate the remaining useful life of the cutting tool. This paper presents a method for tool condition assessment and life prediction. The method is based on nonlinear feature reduction and support vector regression. The number of original features extracted from the monitoring signals is first reduced. These features are then used to learn nonlinear regression models to estimate and predict the level of wear. The method is applied on experimental data taken from a set of cuttings and simulation results are given. These results show that the proposed method is suitable for assessing the wear evolution of the cutting tools and predicting their remaining useful life. This information can then be used by the operators to take appropriate maintenance actions

Deep convolutional neural networks for Bearings failure predictionand temperature correlation

Rolling elements bearings (REBs) is one of the most sensitive components and the common failure unit in mechanical equipment. Bearings failure prognostics, which aims to achieve an effective way to handle the increasing requirements for higher reliability and in the same time reduce unnecessary costs, has been an area of extensive research. The accurate prediction of bearings Remaining Useful Life (RUL) is indispensable for safe and lifetime-optimized operations. To monitor this vital component and planning repair work, a new intelligent method based on Wavelet Packet Decomposition (WPD) and deep learning networks is proposed in this paper. Firstly, features extraction from WPD used as input data. Secondly, these selected features are fed into deep Convolutional Neural Networks (CNNs) to construct the Health Indicator (HI). This study focuses on analysing the relationships such as correlations between the HI and temperature. We develop a solution for the Connectiomics contest dataset of bearings under different operating conditions and severity of defects. The performance of the proposed method is verified by four bearing data sets collected from experimental setup called “PRONOSTIA”. The results show that the health indicator obtains fairly high monotonicity and correlation values and it is beneficial to bearing life prediction. In addition, it is experimentally demonstrated that the proposed method is able to achieve better performance than a traditional neural network based method

On the relevance of preprocessing in predictive maintenance for dynamic systems

The complexity involved in the process of real-time data-driven monitoring dynamic systems for predicted maintenance is usually huge. With more or less in-depth any data-driven approach is sensitive to data preprocessing, understood as any data treatment prior to the application of the monitoring model, being sometimes crucial for the final development of the employed monitoring technique. The aim of this work is to quantify the sensitiveness of data-driven predictive maintenance models in dynamic systems in an exhaustive way. We consider a couple of predictive maintenance scenarios, each of them defined by some public available data. For each scenario, we consider its properties and apply several techniques for each of the successive preprocessing steps, e.g. data cleaning, missing values treatment, outlier detection, feature selection, or imbalance compensation. The pretreatment configurations, i.e. sequential combinations of techniques from different preprocessing steps, are considered together with different monitoring approaches, in order to determine the relevance of data preprocessing for predictive maintenance in dynamical systems

Deep Learning for Fault Diagnosis based on short-time Fourier transform

International audienceThe rapid advancements of the Internet of Things (IoT) enables maintenance strategies to be applied everyday to all sectors, IoT based health management plays an important role. For producing quickly, with high quality while decreasing the risk of production break due to a machine stop, it is necessary to maintain the equipment in a good operational condition. This requirement can be satisfied by the implementation of maintenance strategies for faults detection . In this paper, a novel method called deep learning based on Short-Time Fourier Transform (STFT) is developed for fault diagnosis. An experimental analysis is carried out using a dataset under different operating conditions of speed and loading to substantiate the utility of the proposed strategy. Also a multi-fault deep learning classifier based on STFT is constructed for different faults in this paper. Hence, the purpose is to design an automatic detection system for mechanical components defects based on supervised classification. The diagnosis accuracy assessment is carried out by conducting various experiments on acceleration signals collected from a rotating machinery under different operating conditions

Tool wear condition monitoring based on continuous wavelet transform and blind source sep

International audiencePrognostics and health management (PHM) for condition monitoring systems have been proposed for predicting faults and estimating the remaining useful life (RUL) of components. In fact, in order to produce quickly, economically, with high quality and reduce machine tool downtime, a new intelligent method for tool wear condition monitoring is based on continuous wavelet transform (CWT) and blind source separation (BSS) techniques. CWT is one of the most powerful signal processing methods and has been widely applied in tool wear condition monitoring. The CWT used to transform one set of one-dimensional series into multiple sets of one-dimensional series for preprocessing. After that, BSS was applied to analyze the wavelet coefficients. The signal energy evolution of each independent source obtained by BSS was used for health assessment and RUL estimation, the idea is based on the computation of a nonlinear regression function in a highdimensional feature space where the input data were mapped via a nonlinear function. Experimental results show that the proposed CWT-BSS method can reflect effectively the performance degradation of cutting tools for the milling process. The proposed method is applied on real-world RUL estimation for a given wear limit based on extracted features

Remaining useful life estimation based on nonlinear feature reduction and support vector regression

International audiencePrognostics and Health Management (PHM) of rotating machines is gaining importance in industry and allows increasing reliability and decreasing machines’ breakdowns. Bearings are one of the most components present in mechanical equipments and one of their most common failures. So, to assess machines’ degradations, fault prognostics of bearings is developed in this paper. The proposed method relies on two steps (an offline step and an online step) to track the health state and predict the remaining useful life (RUL) of the bearings. The offline step is used to learn the degradation models of the bearings whereas the online step uses these models to assess the current health state of the bearings and predict their RUL. During the offline step,vibration signals acquired on the bearings are processed to extract features, which are then exploited to learn models that represent the evolution of the degradations. For this purpose, the isometric feature mapping reduction technique (ISOMAP) and support vector regression (SVR) are used. The method is applied on a laboratory experimental degradations related to bearings. The obtained results show that the method can effectively model the evolution of the degradations and predict the RUL of the bearings