Smart devices have become an important entity for many applications in daily life\nactivities. These devices have witnessed a rapid improvement in its technology to\nfulfill the increasingly diverse usage demands. In the meanwhile, rotating machinery\nvibration analysis based on low-cost sensors has gained a considerable attraction over\nthe last few years. For a long time, the vibration analysis of machines has been accepted\nas an effective solution to detect and prevent failures in complex systems to\navoid the sudden malfunction. The objective of this work is to use MEMS accelerometer\nmeasurements to monitor the different level of vibration of a machine. This\nwork presents a new technique for rotating machinery vibration analysis. It uses Fast\nFourier Transformation as a feature extraction algorithm and Fuzzy Logic System\n(FLS) as the classifier algorithm. A smartphone accelerometer is used to collect the\ndata from the vibrating machine. The performance of the proposed technique is\ntested using data from different vibration resources at a different speed of operations.\nThe results are discussed to illustrate the various vibration levels.
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