On the Control of the Technical Condition of Elevator Ropes Based on Artificial Intelligence and Computer Vision Technology

Abstract

Introduction. The safety problem and the situation with accidents during the operation of elevator installations are elucidated. The role of elevator rope defects as a factor of dangerous incidents is indicated from the point of view of statistics. The malfunctions of the elevator mechanical equipment related to the defective indices of the ropes are listed. There is a difference in the documentary fixation of defective indices and rejection rates of ropes of lifting structures. Materials and Methods. The well-known approaches to the control of ropes of lifting structures were described. It was emphasized that visual inspection control (VIC) was required to identify such rejection rates of steel elevator ropes as geometry change, corrosion and wear, wire breaks, temperature exposure, etc. The rejection rate was presented in the form of a mathematical system. The technical condition of elevator ropes during the operation was integrally assessed by the totality of identified defects at a fixed length. The decision to create a software and hardware complex (PAC) for the practical implementation of visual and measuring control was validated. Results. The developed PAC VIC laboratory sample consisted of a hardware part, a video stream processing module, communicator for the server connectivity, specially designed software, and a client mobile application. PAC VIC implemented the following functions: – automatic detection and classification of the major significant rope defects based on a deep convolutional artificial neural network; – demonstration of a three-dimensional image of a rope and an image scanning algorithm with distortion compensation, according to which the metric characteristics of defects were fixed; – integral assessment of the technical condition of the rope according to the totality of detected defects; – color interpretation of the actual technical condition of the rope with subsequent transmission to the user's mobile device. Preliminary tests have shown the suitability of the PAC VIC for identifying defects. The reliability of the results for the identification and qualification of defects exceeded 80%. Work on deep learning of the system continues. Discussion and Conclusions. PAC VIC of elevator ropes provides eliminating the risks of visual control caused by the psychophysical state of a person. It works remotely and contactless. The solution proposed by the authors automatically evaluates the rejection rates according to five criteria: external wire breaks, surface wear, rope diameter change, undulation, traces of temperature exposure. An important result of the VIC of steel ropes using computer vision and artificial intelligence is an increase in reliability and safety during the operation of elevator equipment.