Attribute-driven Fuzzy Fault Tree Model for Adaptive Lubricant Failure Diagnosis

Lubricant diagnosis serves as a crucial accordance for condition-based maintenance(CBM)involving oil changing and wear examination of critical parts in equipment.However,the accuracy of traditional end-to-end diagnosis models is often limited by the inconsistency and random fluctuations in multiple monitoring indicators.To address this,an attribute-driven adaptive diagnosis method is developed,involving three attributes:physicochemical,contamination,and wear.Correspondingly,a fuzzy fault tree(termed FFT)-based model is constructed containing the logic correlations from monitoring indicators to attributes and to lubricant failures.In particular,inference rules are integrated to mitigate conflicts arising from the reverse degradation of multiple indicators.With this model,the lubricant conditions can be accurately assessed through rule-based reasoning.Furthermore,to enhance its intelligence,the model is dynamically optimized with lubricant analysis knowledge and monitoring data.For verification,the developed model is tested with lubricant samples from both the fatigue experiment and actual aero-engines.Fatigue experiments reveal that the proposed model can improve the lubricant diagnosis accuracy from 73.4%to 92.6%compared with the existing methods.While for the engine lubricant test,a high accuracy of 90%was achieved.

Analytical Model and Algorithm of Fuzzy Fault Tree

In the past, the probabilities of basic events were described as triangular or trapezoidal fuzzy number that cannot characterize the common distribution of the primary events in engineering, and the fault tree analyzed by fuzzy set theory did not include repeated basic events. This paper presents a new method to analyze the fault tree by using normal fuzzy number to describe the fuzzy probability of each basic event which is more suitably used to analyze the reliability in safety systems, and then the formulae of computing the fuzzy probability of the top event of the fault tree which includes repeated events are derived. Finally, an example is given.

Fuzzy fault tree analysis of oil and gas leakage in subsea production systems

This paper presents a probabilistic failure analysis of leakage of the oil and gas in a subsea production system using fault tree analysis(FTA).A fault tree was constructed by considering four major areas where the leakages can be initiated.These are:gas and oil wells,pipelines,key facilities and third party damage.Conventional FTA requires precise values for the probability of failure of the basic events.However,since the failure data are uncertain,a fuzzy approach to these data is taken which leads to the so-called fuzzy fault tree analysis(FFTA),a method that employs expert elicitation and fuzzy set theories to calculate the failure probabilities of the intermediate events and the top event through identification of the minimal cut sets of the fault tree.A number of importance measures for minimal cut sets and the basic events have been obtained which helps to identify the nature of dependence of the top event on the basic events and thereby can identify the weakest links that may cause leakage in the subsea production system.

Type-2 fuzzy logic applications designed for active parameter adaptation in metaheuristic algorithm for fuzzy fault-tolerant controller

Purpose-In recent times,fuzzy logic is gaining more and more attention,and this is because of the capability of understanding the functioning of the system as per human knowledge-based system.The main contribution of the work is dynamically adapting the important parameters throughout the execution of the flower pollination algorithm(FPA)using concepts of fuzzy logic.By adapting the main parameters of the metaheuristics,the performance and accuracy of the metaheuristic have been improving in a varied range of applications.Design/methodology/approach-The fuzzy logic-based parameter adaptation in the FPA is proposed.In addition,type2 fuzzy logic is used to design fuzzy inference system for dynamic parameter adaptation in metaheuristics,which can help in eliminating uncertainty and hence offers an attractive improvement in dynamic parameter adaption in metaheuristic method,and,in reality,the effectiveness of the interval type2 fuzzy inference system(IT2 FIS)has shown to provide improved results as matched to type-1 fuzzy inference system(T1 FIS)in some latest work.Findings-One case study is considered for testing the proposed approach in a fault tolerant control problem without faults and with partial loss of effectiveness of main actuator fault with abrupt and incipient nature.For comparison between the type-1 fuzzy FPA and interval type-2 fuzzy FPA is presented using statitical analysis which validates the advantages of the interval type2 fuzzy FPA.The statistical Z-test is presented for comparison of efficiency between two fuzzy variants of the FPA optimization method.Originality/value-The main contribution of the work is a dynamical adaptation of the important parameters throughout the execution of the flower pollination optimization algorithm using concepts of type2 fuzzy logic.By adapting the main parameters of the metaheuristics,the performance and accuracy of the metaheuristic have been improving in a varied range of applications.

Fault Diagnosis for Non-linear System Based On Adaptive Fuzzy System

Although lots of valuable results for fault diagnosis based on model have been achieved in linear system, it is difficult to apply these results to non-linear system due to the difficulty of modeling the non-linear system by analysis. Adaptive Fuzzy system provides a way for solving this problem because it can approximate any non-linear system at any accuracy. The key for adaptive Fuzzy system to solve problem is its learning ability, so the authors present a learning algorithm for Adaptive fuzzy system, which can build the system's model by learning from the measurement data as well as experience knowledge with high accuracy. Furthermore, the experiment using the learning algorithm to model a servo-mechanism and to construct the fault diagnosis system based on the model is carried out, the results is very good.

FAULT DIAGNOSIS EXPERT SYSTEM FOR ROTATING MACHINERY BASED ON A FUZZY PROBABILITY LOGIC INFERENCE MODEL

A new theory- the fuzzy probability logic theory is presented , This theory incorpo- rates the genterally-used fuzzy logic and the traditionally-used probability logic theory in attempt to emulate the rational fault diagnosis under uncertainty. According to the theory , an inference model , named as FSL , is thus designed to be devoted to the building of a fault diagnosis expert system for rotating machinery (ROSLES) . The system is put into operation on a vibration simula- tor stand for 300 MW turbine generator set ( 1 : 1 0) and satisfactory results are gained.

Risk Assessment of Deep-Water Horizontal X-Tree Installation

Due to the high potential risk and many influencing factors of subsea horizontal X-tree installation,to guarantee the successful completion of sea trials of domestic subsea horizontal X-trees,this paper established a modular risk evaluation model based on a fuzzy fault tree.First,through the analysis of the main process oftree down and combining the Offshore&Onshore Reliability Data(OREDA)failure statistics and the operation procedure and the data provided by the job,the fault tree model of risk analysis of the tree down installation was established.Then,by introducing the natural language of expert comprehensive evaluation and combining fuzzy principles,quantitative analysis was carried out,and the fuzzy number was used to calculate the failure probability of a basic event and the occurrence probability of a top event.Finally,through a sensitivity analysis of basic events,the basic events of top events significantly affected were determined,and risk control and prevention measures for the corresponding high-risk factors were proposed for subsea horizontal X-tree down installation.

A new approach to fuzzy dynamic fault tree analysis using the weakest n-dimensional t-norm arithmetic

Dynamic fault tree analysis is widely used for the reliability analysis of the complex system with dynamic failure characteristics. In many circumstances, the exact value of system reliability is difficult to obtain due to absent or insufficient data for failure probabilities or failure rates of components. The traditional fuzzy operation arithmetic based on extension principle or interval theory may lead to fuzzy accumulations. Moreover, the existing fuzzy dynamic fault tree analysis methods are restricted to the case that all system components follow exponential time-to-failure distributions. To overcome these problems, a new fuzzy dynamic fault tree analysis approach based on the weakest n-dimensional t-norm arithmetic and developed sequential binary decision diagrams method is proposed to evaluate system fuzzy reliability. Compared with the existing approach,the proposed method can effectively reduce fuzzy cumulative and be applicable to any time-tofailure distribution type for system components. Finally, a case study is presented to illustrate the application and advantages of the proposed approach.