Research on Lift Fault Prediction and Diagnosis Based on Multi-sensor Information Fusion (2020)

Mi., 15.04.2020 - 11:33 von , zuletzt bearbeitet am 15.04.2020 - 12:06

I am very pleased to refer to a further contribution with my international colleagues in cooperation with CIT (Changshu Institute of Technology). The publications are published by Springer-Verlag in the series Lecture Notes in Electrical Engineering.

This book presents selected papers from the 9th International Workshop of Advanced Manufacturing and Automation (IWAMA 2019), held in Plymouth, UK, on November 21–22, 2019. Discussing topics such as novel techniques for manufacturing and automation in Industry 4.0 and smart factories, which are vital for maintaining and improving economic development and quality of life, it offers researchers and industrial engineers insights into implementing the concepts and theories of Industry 4.0, in order to effectively respond to the challenges posed by the 4th industrial revolution and smart factories.


Nowadays, most of the fault diagnosis methods are based on the collected data, which can not realize the timely prediction of fault diagnosis, and the measurement information based on a single sensor cannot fully and accurately reflect the working status of lift, thus causing the uncertainty and inaccuracy of fault diagnosis. An lift fault diagnosis algorithm based on DS data fusion is proposed. Multi-sensor fusion is used to initialize the initial reliability distribution of the sensor according to the membership degree of each diagnostic category. The data collected by each sensor is taken as evidence body. The final diagnosis results are obtained by data fusion method. Experiments on lift fault diagnosis show that the proposed method can correctly and timely predict the fault, overcome the uncertainty and inaccuracy of single sensor fault diagnosis, and improve the accuracy of lift fault diagnosis and prediction.

Cite this paper as: Jiang X., Namokel M., Hu C., Tian R. (2020) Research on Lift Fault Prediction and Diagnosis Based on Multi-sensor Information Fusion. In: Wang Y., Martinsen K., Yu T., Wang K. (eds) Advanced Manufacturing and Automation IX. IWAMA 2019. Lecture Notes in Electrical Engineering, vol 634. Springer, Singapore, pp 160-168.

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