5 results about "Repair rate" patented technology

This invention discloses a dimension-reduction reliability assessment method for switched reluctance motors (SRMs) that considers repairability. This method analyzes the faults of the SRM at structural levels, comprehensively converting the failure probabilities of each underlying component into overall system state transition parameters. Based on this, the repairability rate is calculated according to the actual periodic maintenance cycle of the equipment, and a Markov state transition equation system containing maintenance recovery paths is established. To avoid complex calculus operations involving high-order matrices, this invention utilizes Laplace transform to convert the dynamic process into algebraic calculations in the complex frequency domain, achieving analytical order reduction of the model. By performing algebraic analysis on the complex frequency domain function, the dynamic reliability function and mean time between failures (MTBF) of the system are directly obtained. This invention overcomes the shortcomings of traditional models, such as cumbersome computation and unrealistic lifespan predictions. While effectively reducing the computational complexity of microcontrollers, it significantly improves the accuracy of lifespan prediction for motor drive systems and enhances their engineering application value.

The invention relates to the technical field of power system reliability evaluation, and particularly provides a Markov model-based substation-level protection system reliability evaluation method and device, and the method comprises the steps: obtaining a historical failure rate, a historical fault recovery rate and a historical fault self-detection rate corresponding to target equipment in a target system; the target system comprises a plurality of subsystems of different types, and each subsystem comprises a plurality of target devices; calculating the system failure rate and the system repair rate of the corresponding subsystem according to the historical failure rate and the historical fault repair rate of the target equipment; the system repair rate comprises a hardware repair rate and a software repair rate; calculating the state transition probability and the state probability of the target system according to the system failure rate, the hardware repair rate and the software repair rate of the subsystem; and determining a corresponding reliability evaluation result according to the state probability of the target system. The method comprehensively considers the fault mode and the self-inspection influence, and effectively improves the evaluation precision.

The application discloses a kind of maintenance strategy formulation method and system for improving the reliability of stability control system, comprising the following steps: first, collect system-level abnormal fault data in stability control system, establish fault tree model according to fault data, analyze the influence of potential failure of each component in stability control system on the reliability of overall stability control system;Second, based on the constructed fault tree model, the improved Apriori algorithm is used to analyze the influencing factors of potential failure in stability control system, and strong association rules between influencing factors and potential failure are constructed, to provide a theoretical basis for maintenance strategy to reduce failure rate;Finally, the maintenance strategy is optimized using Markov model method to improve the failure repair rate, and the stability control system failure repair time is reduced by finite time optimization control algorithm.The method of the present application aims to reduce the failure rate and failure repair time of stability control system to improve the reliability of stability control system.

The application provides a fluidized bed equipment group operation and maintenance method and system for production of silicon-carbon negative electrode materials, and relates to the field of intelligent operation and maintenance of equipment. The method comprises the following steps: fusing vibration data, temperature gradient data and internal pressure fluctuation data of the pretreated fluidized bed by a Kalman filtering algorithm and generating a state feature vector; inputting the state feature vector into a life prediction model to calculate the remaining life and maintenance degree, calculating the real-time repair rate based on a Bayesian updating algorithm, analyzing the repair rate fluctuation coefficient, and adjusting the real-time repair rate to a stable value through standardization operation when the repair rate fluctuation coefficient reaches a preset fluctuation coefficient threshold; constructing a total downtime cost function through an M / M / c queuing model, and solving the optimal number of maintenance personnel through the Lagrange multiplier method. The application can realize fine operation and maintenance and dynamic scheduling of the fluidized bed equipment group for production of silicon-carbon negative electrode materials, effectively improve the equipment group operation and maintenance efficiency, reduce downtime cost, and ensure the stability and reliability of the production process.

The application relates to an aircraft state-based spare part dynamic planning method and system, and belongs to the technical field of aircraft spare part planning. The fleet scale and the fleet annual utilization rate of a machine type to which a to-be-predicted component belongs are acquired, and historical fault data of the to-be-predicted component are acquired, so as to calculate the working component quantity, the fault rate, the repair rate and the spare part support rate of the to-be-predicted component. Then, the working component quantity, the fault rate, the repair rate and the spare part support rate are taken as inputs, a genetic algorithm is used to optimize and solve a spare part quantity calculation model, and the spare part demand quantity of the to-be-predicted component in a prediction time period is obtained. The minimum spare part demand quantity of the to-be-predicted component in the prediction time period that meets a given spare part support rate can be obtained, and the prediction precision is high.