Failure Prediction of Temperature Sensor Using Hybrid Neural Fuzzy

Zusatztext

The growth of Electronic products becomes more complex due to a major requirement of high reliability, high speed and low cost. In todays world, reliability becomes a great need of any electronic equipment for active as well as passive components such as a temperature sensor. Failure prediction is the major constraints to predict the remaining useful life of the component in order to anticipate the costly failures or system unavailability. In the modern competitive market, low cost and high performance are the key factors to attract the customers towards their products. Growing system complexity demands robust control to reduce system control and to reduce the successive failures. Reliability prediction of passive components especially temperature sensor is of great concern as these are required in almost every system. As these components are mounted on a board to form a complete system, the probability of damage is increased, as the different components have different characteristics and different operating conditions. So artificial intelligence techniques are used which adopt knowledge of failure mechanism of an individual part of the system and check the health condition of it.

Autorenportrait

Cherry Bhargava is working as an Assistant Professor and Head, VLSI Domain, Lovely Professional University, India. She is an alumnus of Thapar University, Patiala. She has submitted Ph.D. at IKGPTU. Pardeep Kumar Sharma is working as Assistant Professor and pursuing Ph.D. from LPU. Sahib Ali is a research scholar at LPU.

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