# Reliability Analysis of Military Vehicles Based on Censored Failures Data
## 1 Introduction
Its design is based on a hybridisation of dynamic weight particle swarm optimisation-based sine map and back propagation neural network. In publication [22], a reliability prediction model was developed based on a cascade neural network. A sigmoidal hidden layer activation function along with linear output neuron activation was proposed. The model predicted the value of the reliability function for Computerized Numerical Control (CNC) machines depending on the mean time between failures (MTBF) with an accuracy measured through Mean Absolute Percentage Error (_MAPE_), ranging from 1.56% to 2.53%. An approach based on the use of a three-layer neural network and remanufacturing coefficients is presented in [23]. Reliability indices for the individual components of the technical system were determined based on the average times between failures. The flexibility of artificial neural networks in fitting degradation paths has been highlighted in publications [24,25]. The authors of these studies used MLP supported by a stochastic process [24] and a Weiner process [25]. Validations of both models were performed using simulated data and spindle systems wear data. For the actual data, the mean absolute relative error for all test trials did not exceed 0.13 in [24] and 0.06 in [25]. A much more structurally complex convolutional neural network was developed by the authors of [26]. The proposed model, when validated with data from three power systems, achieved significantly better fitting results than MLP, SVM (Support Vector Machine) and KNN (K-Nearest Neighbors) models. In Table 1, the models discussed above, which have been tested using turbocharger and engine datasets, as well as models for predicting the reliability of other technical objects, are summarised in chronological order. Military vehicles are technical objects which are submitted to the renewal of their technical worthiness after its loss due to the failure incurred. The system of operation also entails periodic maintenance to renew the technical service life. This is an action aimed at maintaining the technical readiness of vehicles at a sufficiently high level. However, due to the extreme operating conditions and long service life, unplanned periods of technical inoperability of the vehicle fleet due to frequent breakdowns are a real problem for the transport system of military units. To date, reliability studies of military-technical facilities have mainly been conducted using mathematical statistics and probability calculation. This publication proposes a reliability testing methodology based on the use of numerical methods and artificial neural networks. With the developed algorithm, it is possible to model and analyse the reliability characteristics of a specific set of technical objects on the basis of observations of a certain period of exploitation with consideration of censored data. The research was conducted on a selected sample of military vehicles carrying out transport tasks within the transport system of a given military unit. The proposed study focuses on technical objects and, from that point of view, is original research and have not yet been the subject of research in the present approach published by