Maintenance is essential in all kinds of machines. In past, the machine operators would recognize the machine condition by touching the machine or hearing the machine operating sound. However, this is too subjective and not effective for inexperienced operators. In fact, most modern machineries are so complex that many components may run together, making the operator impossible to distinguish the difference between a normal and anomalous machine. Although more scientific fault diagnostic systems are available, they are expensive and difficult to use without comprehensive learning. Therefore, there is a need from industry to have an economy and efficient machine fault diagnostic system. The occurrence of fault must be identified as early as possible to avoid fatal breakdown of machines. The aim of developing the Smart Asset Maintenance System (SAMS) is to provide a portable and comprehensive but low-cost and simple-to-use solution for the industry to perform equipment maintenance effectively.

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