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Self-Aligning Troughing Roller (SATR) is one of the critical components in belt conveyor; it is a very critical component in riding the belt conveyor in fault free condition. SATR arrangement has a long roll to support the given belt and handle maximum load per cross-section. SATR has machine elements like ball bearing, central shaft and the external shell. In belt conveyor system certain faults such as bearing fault (BF), central shaft fault (SF), combined bearing flaw and central shaft fault (BF& SF) occur frequently. Fault diagnosis in SATR essentially forms a classification problem. A prototype setup has been designed and fabricated; Different faults such as bearing fault (BF), central shaft fault (SF), combined bearing flaw and central shaft fault (BF& SF) are introduced one at a time and the corresponding vibration signals have been acquired from the setup . Followed by this step a set if statistical parameters were computed which forms the feature set and classified using Artificial Neural Network (ANN) algorithms and decision tree algorithms. At the outset, decision tree algorithm shows superior performance in terms of classification accuracy. The whole effort is to bring out the best number of features for maximum efficiency. A tenfold cross validation was performed to validate the results.

Multi-component fault diagnosis of Self Aligning Troughing Roller (SATR) in belt conveyor system using decision tree: A statistical approach