Gear classification and fault detection using a diffusion map framework
نویسندگان
چکیده
A system health monitoring scheme using diffusion map is proposed. Diffusion map reduces the dimensionality of measurement data. This facilitates the comparison of newly arriving measurements to the known training data. The method is trained and tested with real gear monitoring data. The results show that data recordings can be classified as working or broken using dimensionality reduction.
منابع مشابه
Developing A Fault Diagnosis Approach Based On Artificial Neural Network And Self Organization Map For Occurred ADSL Faults
Telecommunication companies have received a great deal of research attention, which have many advantages such as low cost, higher qualification, simple installation and maintenance, and high reliability. However, the using of technical maintenance approaches in Telecommunication companies could improve system reliability and users' satisfaction from Asymmetric digital subscriber line (ADSL) ser...
متن کاملFault Detection and Classification in Double-Circuit Transmission Line in Presence of TCSC Using Hybrid Intelligent Method
In this paper, an effective method for fault detection and classification in a double-circuit transmission line compensated with TCSC is proposed. The mutual coupling of parallel transmission lines and presence of TCSC affect the frequency content of the input signal of a distance relay and hence fault detection and fault classification face some challenges. One of the most effective methods fo...
متن کاملA Novel Fault Detection and Classification Approach in Transmission Lines Based on Statistical Patterns
Symmetrical nature of mean of electrical signals during normal operating conditions is used in the fault detection task for dependable, robust, and simple fault detector implementation is presented in this work. Every fourth cycle of the instantaneous current signal, the mean is computed and carried into the next cycle to discover nonlinearities in the signal. A fault detection task is complete...
متن کاملFault 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...
متن کاملClassification of damage for planetary gear of wind turbine simulator
ABSTRACT A planetary gear of a wind turbine is a critical component in the view of condition monitoring and fault detection because the fault of the gear needs much cost and time to fix or replace it. In this paper, classification of damage for a planetary gear is proposed and validated by the experiment of wind turbine simulator in order to evaluate the possibility of the application to fault ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Pattern Recognition Letters
دوره 53 شماره
صفحات -
تاریخ انتشار 2015