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Unexpected failures of industrial gearboxes may cause significant economic losses. It is therefore important to detect early fault symptoms. This paper introduces signal processing methods based on approximate entropy (ApEn) and Lempel-Ziv Complexity (LZC) for defect detection of gears. Both methods are statistical measurements exploring the regularity of a vibratory signal. Applied to gear signals, the parameter selection of ApEn and LZC calculation are first numerically investigated, and appropriate parameters are suggested. Finally, an experimental study is presented to investigate the effectiveness of these indicators. The results demonstrate that ApEn and LZC provide alternative features for signal processing. A new methodology is presented combining both Kurtosis and LZC for early detection of faults. The results show that this proposed method may be used as an effective tool for early detection of gear faults

Monitoring gears by vibration measurements: Lempel-Ziv complexity and Approximate Entropy as diagnostic tools