Condition Monitoring and Fault Diagnosis for an Antifalling Safety Device

Abstract

There is a constant need for the safe operation and reliability of antifalling safety device (AFSD) of an elevator. This paper reports an experimental study on rotation speed and catching torque monitoring and fault diagnosis of an antifalling safety device in a construction elevator. Denoising the signal using wavelet transform is presented in this paper. Based on the denoising effects for several types of wavelets, the sym8 wavelet basis, which introduces the high order approximation and an adaptive threshold, is employed for denoising the signal. The experimental result shows a maximum data error reduction of 7.5% is obtained and SNRs (signal-to-noise ratio) of rotation speed and catching torque are improved for 3.9% and 6.4%, respectively.

Fuzzy Multicriteria Model for Selection of Vibration Technology

“…Furthermore, its versatility with regard to the machines it can be applied to and the typology of faults or anomalies that can be detected [6], along with the possibility of developing an automatic diagnostic system [7], have made it the most widely used predictive method [8]. This has led to a large number of contributions in the literature [3], analysing new diagnostic techniques [9,10], many case studies of fault diagnosis in different components and types of machinery [5,7,11,12], and studies of prognostics or fault prediction before it occurs [4].…”