Measuring in-service traction elevator reliability based on orthogonal defect classification and Markov analysis

To solve the challenge of accurate in-service traction elevator failure prediction, maintenance cycle and steady availability, a novel reliability model is proposed which combines orthogonal defect classification (ODC) and Markov analysis (MA). The elevator failure data are classified by the ODC method. Then, the failure rate and maintenance rate of the elevator parts are obtained based on triangular fuzzy theory. By analyzing the maintenance function of each part, the optimum maintenance cycle of the elevator is determined. Finally, the transient state and steady state equations are established by MA to determine the steady availability of elevators. A case study on elevator accidents and failure data is used to validate the effectiveness of the proposed method. The results show that the system steady state availability of elevators in the study was 0.9002.