Reliability Analysis of Military Vehicles Based on Censored Failures Data

Abstract

The paper proposes a methodology of reliability testing as applied to vehicles used in military transport systems. After estimating the value of the reliability function using the Kaplan–Meier estimator, reliability models were developed and analysed. The neural model, which achieved the value of the correlation coefficient R exceeding 0.99, was determined to fit the empirical data the best. On the basis of the approximated reliability function of several models, the reliability characteristics of the tested sample of vehicles were determined. Plots of the failure probability density function for all three models had similar courses over a significant part of the function domain. A failure intensity function was also determined, which varied between models. For the exponential and Weibull model, the expected mileage between failures was calculated, which proved impossible for the neural model. The proposed methodology is capable of modelling reliability characteristics based on the observation of an assumed period of the exploitation process of the selected group of military vehicles.

Semi-Markov approach for reliability modelling of light utility vehicles

“…A fuzzy model with a similar accuracy reflected the values of an empirical reliability function. In turn, in [34], the multilayer perceptron (MLP) neural model was slightly better at approximating the light utility vehicle reliability function relative to the exponential and Weibull distributions. Whereas, in the case of fluid filling equipment in the automotive manufacturing industry, Soltanali et al [45] demonstrated a significant improvement in the accuracy of reliability predictions using the Adaptive Neuro-Fuzzy Inference System model, compared to Weibull and Non-Homogeneous Poisson Process models.…”

“…Statistical methods Generalized Gamma distribution Cabin door lock on rail vehicles [43] Reliability series structure model Aircraft commutators [53] Statistical methods and fuzzy logic Weibull and fuzzy models Light combat aircraft [60] Statistical methods and neural networks Exponential, Weibull and MLP models Light utility vehicles [34] Weibull, Non-Homogeneous Poisson Process and Adaptive Neuro-Fuzzy Inference System models Fluid filling equipment in automotive manufacturing industry [45] Neural networks Neural models (MLP) Vehicles [25] Neural model of reliability allocation Machine tools [11] Neural networks and partial swarm optimization Hybrid model Industrial robot systems [2] Ordered weighted averaging aggregation operator Reliability allocation model Thin-film transistor liquid-crystal display [7] Probabilistic methods Reliability models including hard and soft failures Micro-electro-mechanical systems devices [46][37]…”

“…The reliability function R(t) determines the probability of an event, in which a technical object operated under assumed conditions remains continuously in a state of technical suitability from time 0 to time t [1,16,17,34]. The mathematical reliability function description is presented by the dependence (9): 𝑅(𝑡) = 𝑃(𝑇 ≥ 𝑡)for 𝑡 ≥ 0, (9) where T is the failure time of the technical object.…”

“…If an embedded Markov chain exhibits ergodicity and there are expected values E(Ti) of state sojourn times, the values of ergodic probabilities pj for a semi-Markov process are determined using the dependence (34)(35):…”

Analysis of Light Utility Vehicle Readiness in Military Transportation Systems Using Markov and Semi-Markov Processes

“…The fleet of reliable vehicles is one of the main factors determining the high quality and timely implementation of processes in modern transport systems. The condition for the effective operation of the system is to maintain the means of transport in a state of technical efficiency and be ready to perform tasks [1,2]. The fulfilment of this condition is possible thanks to the organization of operating systems with appropriate technical resources to carry out processes of diagnosis, servicing and repair of vehicles.…”