Fuzzy Logic in Aircraft Onboard Systems Reliability Evaluation—A New Approach

Abstract

This paper is a continuation of research into the possibility of using fuzzy logic to assess the reliability of a selected airborne system. The research objectives include an analysis of statistical data, a reliability analysis in the classical approach, a reliability analysis in the fuzzy set theory approach, and a comparison of the obtained results. The system selected for the investigation was the aircraft gun system. In the first step, after analysing the statistical (operational) data, reliability was assessed using a classical probabilistic model in which, on the basis of the Weibull distribution fitted to the operational data, the basic reliability characteristics were determined, including the reliability function for the selected aircraft system. The second reliability analysis, in a fuzzy set theory approach, was conducted using a Mamdani Type Fuzzy Logic Controller developed in the Matlab software with the Fuzzy Logic Toolbox package. The controller was designed on the basis of expert knowledge obtained by a survey. Based on the input signals in the form of equipment operation time (number of flying hours), number of shots performed (shots), and the state of equipment corrosion (corrosion), the controller determines the reliability of air armament. The final step was to compare the results obtained from two methods: classical probabilistic model and fuzzy logic. The authors have proved that the reliability model using fuzzy logic can be used to assess the reliability of aircraft airborne systems.

Semi-Markov approach for reliability modelling of light utility vehicles

“…The results obtained by statistical methods also constitute the foundation for the evaluation of advanced models developed based on the application of fuzzy logic and neural networks. Żyluk et al [60] developed statistical reliability models for lightweight combat aircraft, with the Weibull model turning out to be the best match. A fuzzy model with a similar accuracy reflected the values of an empirical reliability function.…”

“…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]…”

“…It was assumed that at time t = 0 a vehicle was in full technical suitability, therefore, it remained in state S1. The initial distribution vector pj(0), which describes the assumed adopted, has been presented using formula (60):…”

Selected Issues Associated with the Operational and Power Supply Reliability of Fire Alarm Systems

“…In addition, a power supply is equipped with overvoltage systems and filters reducing interference associated with ensuring FAS electromagnetic compatibility [96,101,102]. Unfitness or lack of main power supply is reported to an alarm-receiving-centre or service groups as a fail-safe (at that time, the system switches to backup power, e.g., from a battery bank) [96,[103][104][105]. Only alarm (fire event within a facility) and failure (system unfit over a specified area) signals are sent to the State Fire Service via two independent teletransmission channels, to make sure the information reaches the recipient [1,106,107].…”

Comprehensive Engineering Frequency Domain Analysis and Vibration Suppression of Flexible Aircraft Based on Active Disturbance Rejection Controller

“…Han et al proposed a controller combining SMC and reinforcement learning to achieve greater robustness and control accuracy [22]. FLC is also an essential player in modern control theory, and its performance has been widely discussed in the aerospace control field [23][24][25][26]. However, the researchers realized that there are several problems with the above algorithms, such as the PID algorithm seems too simple to adapt to industry application.…”