Fault diagnosis studies of face milling cutter using machine learning approach

Using Wavelet Support Vector Machine for Fault Diagnosis of Gearboxes

Identifying fault categories, especially for compound faults, is a challenging task in mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method based on wavelet packet transform (WPT) and multiple classifier fusion. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the...

Robust Fault Diagnosis in Electric Drives Using Machine Learning

The power electronics inverter can be considered as the weakest link in an electric drive system, hence the focus of this research work is on the detection of fault conditions of the inverter. A machine learning framework is developed to systematically select torque-speed domain operation points, which in turn are fed to an electric drive model to generate signals for training an artificial neu...

Grid Application Fault Diagnosis Using Wrapper Services and Machine Learning

With increasing size and complexity of Grids manual diagnosis of individual application faults becomes impractical and timeconsuming. Quick and accurate identification of the root cause of failures is an important prerequisite for building reliable systems. We describe a pragmatic model-based technique for application-specific fault diagnosis based on indicators, symptoms and rules. Customized ...

Study of the effective cutter radius for end milling of free-form surfaces using a torus milling cutter

Fault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods

Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...