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With the abundance of data in the current era of the industrial revolution, it would be essential to have software with offline data security features to perform data-based analysis of equipment life by identifying the factors affecting equipment failure, which leads to the loss of production. Therefore, this project aims to develop software that can identify the equipment’s health state based on reliability analysis considering the critical factors. The software will first use historical failure data to determine the essential factors influencing the performance or deterioration of the equipment and later use them to provide more accurate reliability predictions. The developed software would be simple and easy to use, won’t require much human effort from maintenance staff to input data, and later could be integrated with the available sensor using some data acquisition system in a safer environment.
Xihui Liang
Springboard Atlantic Inc.
Engineering
Aerospace; Automotive; Manufacturing and Construction
University of Manitoba
Accelerate
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