A Condition Monitoring Method via a New Signal Expansion Strategy for the Crystal Lifting and Rotating Mechanism

The crystal lifting and rotating mechanism (CLRM) is the key motion device during the growth process of monocrystalline silicon. The operation state of CLRM has a direct influence on the quality of the monocrystalline silicon. Typically, the CLRM operates at a slow speed with subtle changes in state and inconspicuous signal features, which makes it hard to effective diagnosis the working condition. In this paper, a vibration-signal-based diagnosis method is proposed to monitor the operation status of the CLRM. Firstly, the vibration signals are collected by the sensor installed on the certain location of the CLRM. A signal expansion strategy is then designed to extent the original signal by integration of variational mode decomposition and canonical polyadic decomposition. The characteristic of the signal is enriched. After that, the features of the expanded signals are extracted using permutation entropy, followed by the K-nearest neighbor classification. Three representative experiments are conducted to verify the performance of the proposed method using different datasets, including the benchmark vibration signal dataset, signals acquired from the experimental platform established by our laboratory, and the signals acquired during the actual growth process of monocrystalline silicon.