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

Controller Area Network

Controller Area Network (CAN) is a popular and very well-known bus system, both in academia and in industry. CAN protocol was introduced in the mid eighties by Robert Bosch GmbH [7] and it was internationally standardized in 1993 as ISO 11898-1 [24]. It was initially designed to distributed automotive control systems, as a single digital bus to replace traditional point-to-point cables that were growing in complexity, weight and cost with the introduction of new electrical and electronic systems. Nowadays CAN is still used extensively in automotive applications, with an excess of 400 million CAN enabled microcontrollers manufactured each year [14]. The widespread and successful use of CAN in the automotive industry, the low cost asso- ciated with high volume production of controllers and CAN's inherent technical merit, have driven to CAN adoption in other application domains such as: industrial communications, medical equipment, machine tool, robotics and in distributed embedded systems in general. CAN provides two layers of the stack of the Open Systems Interconnection (OSI) reference model: the physical layer and the data link layer. Optionally, it could also provide an additional application layer, not included on the CAN standard. Notice that CAN physical layer was not dened in Bosch original specication, only the data link layer was dened. However, the CAN ISO specication lled this gap and the physical layer was then fully specied. CAN is a message-oriented transmission protocol, i.e., it denes message contents rather than nodes and node addresses. Every message has an associated message identier, which is unique within the whole network, dening both the content and the priority of the message. Transmission rates are dened up to 1 Mbps. The large installed base of CAN nodes with low failure rates over almost two decades, led to the use of CAN in some critical applications such as Anti-locking Brake Systems (ABS) and Electronic Stability Program (ESP) in cars. In parallel with the wide dissemination of CAN in industry, the academia also devoted a large eort to CAN analysis and research, making CAN one of the must studied eldbuses. That is why a large number of books or book chapters describing CAN were published. The rst CAN book, written in French by D. Paret, was published in 1997 and presents the CAN basics [32]. More implementation oriented approaches, including CAN node implementation and application examples, can be found in Lorenz [28] and in Etschberger [16], while more compact descriptions of CAN can be found in [11] and in some chapters of [31]. Despite its success story, CAN application designers would be happier if CAN could be made faster, cover longer distances, be more deterministic and more dependable [34]. Over the years, several protocols based in CAN were presented, taking advantage of some CAN properties and trying to improve some known CAN drawbacks. This chapter, besides presenting an overview of CAN, describes also some other relevant higher level protocols based on CAN, such as CANopen [13], DeviceNet [6], FTT-CAN [1] and TTCAN [25]

Multi-layer contribution propagation analysis for fault diagnosis

The recent development of feature extraction algorithms with multiple layers in machine learning and pattern recognition has inspired many applications in multivariate statistical process monitoring. In this work, two existing multilayer linear approaches in fault detection are reviewed and a new one with extra layer is proposed in analogy. To provide a general framework for fault diagnosis in succession, this work also proposes the contribution propagation analysis which extends the original definition of contribution of variables in multivariate statistical process monitoring. In fault diagnosis stage, the proposed contribution propagation analysis for multilayer linear feature extraction algorithms is compared with the fault diagnosis results of original contribution plots associated with single layer feature extraction approach. Plots of variable contributions obtained by the aforementioned approaches on the data sets collected from a simulated benchmark case study (Tennessee Eastman process) as well as an industrial scale multiphase flow facility are presented as a demonstration of the usage and performance of the contribution propagation analysis on multilayer linear algorithms

Maximal information-based nonparametric exploration for condition monitoring data

The system condition of valuable assets such as power plants is often monitored with thousands of sensors. A full evaluation of all sensors is normally not done. Most of the important failures are captured by established algorithms that use a selection of parameters and compare this to defined limits or references. Due to the availability of massive amounts of data and many different feature extraction techniques, the application of feature learning within fault detection and subsequent prognostics have been increasing. They provide powerful results. However, in many cases, they are not able to isolate the signal or set of signals that caused a change in the system condition. Therefore, approaches are required to isolate the signals with a change in their behavior after a fault is detected and to provide this information to diagnostics and maintenance engineers to further evaluate the system state. In this paper, we propose the application of Maximal Information-based Nonparametric Exploration (MINE) statistics for fault isolation and detection in condition monitoring data. The MINE statistics provide normalized scores for the strength of the relationship, the departure from monotonicity, the closeness to being a function and the complexity. These characteristics make the MINE statistics a good tool for monitoring the pair-wise relationships in the condition monitoring signals and detect changes in the relationship over time. The application of MINE statistics in the context of condition monitoring is demonstrated on an artificial case study. The focus of the case study is particularly on two of the MINE indicators: the Maximal information coefficient (MIC) and the Maximum Asymmetry Score (MAS). MINE statistics prove to be particularly useful when the change of system condition is reflected in the relationship between two signals, which is usually difficult to be captured by other metrics