A Learning Variable Neighborhood Search Approach for Induction Machines Bearing Failures Detection and Diagnosis

This paper proposes a three-phase metaheuristic-based approach for induction machine bearing failure detection and diagnosis. It consists of extracting and processing different failure types features to set up a knowledge base, which contains different failure types. The first phase consists in pre-processing the measured signals by aggregating them and preparing the data in exploitable formats for the clustering. The second phase ensures the induction machine operating mode diagnosis. A measured signals clustering is performed to build classes where each one represents a health state. A variable neighborhood search (VNS) metaheuristic is designed for data clustering. Moreover, VNS is hybridized with a classical mechanics-inspired optimization (CMO) metaheuristic to balance global exploration and local exploitation during the evolutionary process. The third phase consists of two-step failure detection, setting up a knowledge base containing different failure types, and defining a representative model for each failure type. In the learning step, different class features are extracted and inserted in the knowledge base to be used during the decision step. The proposed metaheuristic-based failure detection diagnosis approach is evaluated using PRONOSTIA and CWR bearing data experimental platforms vibration and temperature measurements. The achieved results clearly demonstrate the failure detection and diagnosis, efficiency, and effectiveness of the proposed metaheuristic approach.

Network Failure Detection and Diagnosis by Analyzing Syslog and SNS Data: Applying Big Data Analysis to Network Operations

We present two major information examination strategies for diagnosing the reasons for system disappointments and for identifying system disappointments early. Syslogs contain log information created by the framework. We dissected syslogs what's more, prevailing with regards to distinguishing the reason for a system disappointment via consequently learning more than 100 million logs without requiring any past learning of log information. Investigation of the information of an interpersonal interaction benefit (in particular, Twitter) empowered us to recognize conceivable system disappointments by extricating system disappointment related tweets, which represent under 1% of all tweets, continuously and with high exactness.