Papers by Keyword: Breakdown Diagnosis

Abstract: A kind of breakdown diagnosis algorithm was proposed based on Ant colony algorithm. The problem of fault identification and classification was translated to a constrained optimized clustering problem under certain conditions in this algorithm. It was applied to the process of fault diagnosis of a hydraulic system. The results show the algorithm is valid and reasonable.

Abstract: In view of the family use wireless router in network turning on and the use process common breakdown, this paper systematically analyzes the wireless router causes of network failure, Six methods proposed to solve home wireless network router failure methods and procedures, And wireless network management and maintenance skills of daily, Shows the configuration of the wireless router and network connection troubleshooting typical case, Designed to help users quickly resolve wireless network failure.

Abstract: High Speed Brushes Aeration Mechanics are the effective aeration equipments which are widely used in the environmental protection. Because of the big span of main spindle and its high speed when it is working, the breakdown sometimes occurs. It is very importance to monitor its condition and diagnose its breakdowns. Turbulent Flow Displacement Sensors are the non-contact types which are based on eddy current effect. It has many advantages, such as good linearity, wide frequency response scope, convenience installment and so on. So it is very suitable for the main spindle’s vibration signals of a high speed brushes aeration mechanic are monitored. With the development of Artificial Neural Networks technology, the equipment breakdown diagnosis has realized intellectualization. The recognition of equipment failure types is one of the most important studying domains of Artificial Neural Networks at present. Based on the research of eddy current effect and Artificial Neural Networks, we build up a test system which can monitor condition and diagnose breakdown to a GSB-12 high speed brushes aeration mechanic. With the help of it, the vibration signals of the measurement points on the main spindle are measured at two mutually vertical positions. The signals’ base frequency and multiplicative frequency are taken as characteristic value. Six common breakdowns are selected and to be taken as the standard sample and there are 3 lays in the neural network. Using FBP algorithm, we get a satisfied effect. The experiment has confirmed that this method is advanced, reliable and practical. It provides a new method about intelligent monitor and breakdown diagnosis to high speed brushes aeration mechanics’ condition.