Condition Monitoring of Roller Bearing by K-star Classifier and K-nearest Neighborhood Classifier Using Sound Signal

• 作者: Hemantha Kumar M.Amarnath Rahul Kumar Sharma V.Sugumaran
• 来源期刊: 结构耐久性与健康监测(英文)
• 学科: 工学
• 关键词: K-star k-nearest neighborhood K-NN machine learning approach condition monitoring fault diagnosis roller bearing DECISION TREE ALGORITHM J-48 random TREE ALGORITHM DECISION making two-layer feature selection sound signal statistical features
• 年，卷（期）: 2017,（1）
• 页码范围: 1-17
• 页数: 17页
• 分类号: TP1

摘要

Most of the machineries in small or large-scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost of transducer used to acquire vibration signal (Accelerometer). This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is performed from acquired sound signals. Then two-layer feature selection is done using J48 decision tree algorithm and random tree algorithm. These selected features were classified using K-star classifier and k-nearest neighborhood classifier and parametric optimization is performed to achieve the maximum classification accuracy. The classification results for both K-star classifier and k-nearest neighborhood classifier for condition monitoring of roller bearing using sound signals were compared.