COMPUTER-AIDED BLOCK ASSEMBLY PROCESS PLANNING IN SHIPBUILD-ING BASED ON RULE-REASONING

Computer-aided block assembly process planning based on rule-reasoning are developed in order to improve the assembly efficiency and implement the automated block assembly process planning generation in shipbuilding. First, weighted directed liaison graph （WDLG） is proposed to represent the model of block assembly process according to the characteristics of assembly relation, and edge list （EL） is used to describe assembly sequences. Shapes and assembly attributes of block parts are analyzed to determine the assembly position and matched parts of parts used frequently. Then, a series of assembly rules are generalized, and assembly sequences for block are obtained by means of rule reasoning. Final, a prototype system of computer-aided block assembly process planning is built. The system has been tested on actual block, and the results were found to be quite efficiency. Meanwhile, the fundament for the automation of block assembly process generation and integration with other systems is established.

Active suspension control of a one-wheel car model using single input rule modules fuzzy reasoning and a disturbance observer

This paper presents the construction of an active suspension control of a one-wheel car model using fuzzy reasoning and a disturbance observer. The one-wheel car model to be treated here can be approximately described as a nonlinear two degrees of freedom system subject to excitation from a road profile. The active control is designed as the fuzzy control inferred by using single input rule modules fuzzy reasoning, and the active control force is released by actuating a pneumatic actuator. The excitation from the road profile is estimated by using a disturbance observer, and the estimate is denoted as one of the variables in the precondition part of the fuzzy control rules. A compensator is inserted to counter the performance degradation due to the delay of the pneumatic actuator. The experimental result indicates that the proposed active suspension system improves much the vibration suppression of the car model. Key words One-wheel car model - Active suspension system - Single input rule modules fuzzy reasoning - Pneumatic actuator - Disturbance observer Document code A CLC number TH16

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.

On the evidential reasoning rule for dependent evidence combination

Evidential Reasoning(ER)rule,which can combine multiple pieces of independent evidence conjunctively,is widely applied in multiple attribute decision analysis.However,the assumption of independence among evidence is often not satisfied,resulting in ER rule inapplicable.In this paper,an Evidential Reasoning rule for Dependent Evidence combination(ERr-DE)is developed.Firstly,the aggregation sequence of multiple pieces of evidence is determined according to evidence reliability.On this basis,a calculation method of evidence Relative Total Dependence Coefficient(RTDC)is proposed using the distance correlation method.Secondly,as a discounting factor,RTDC is introduced into the ER rule framework,and the ERr-DE model is formulated.The aggregation process of two pieces of dependent evidence by ERr-DE is investigated,which is then generalized to aggregate multiple pieces of non-independent evidence.Thirdly,sensitivity analysis is carried out to investigate the relationship between the model output and the RTDC.The properties of sensitivity coefficient are explored and mathematically proofed.The conjunctive probabilistic reasoning process of ERr-DE and the properties of sensitivity coefficient are verified by two numerical examples respectively.Finally,the practical application of the ERr-DE is validated by a case study on the performance assessment of satellite turntable system.

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

Regression model for estimation of attribute reliability

In this paper,a regression model is developed to estimate attribute reliability in the evidential reasoning(ER)context.By analysing the difference between attribute weight and attribute reliability,a general qualitative definition of attribute reliability is provided.The reliability of an attribute is quantitatively measured in consistence with the qualitative definition in the context of the ER approach.A regression model is then constructed to generate attribute reliabilities by minimising the maximum differences between the real value of attribute reliability and its estimation.Within the post-optimal solution space of attribute reliabilities,an optimisation model is constructed to determine the expected utilities of each alternative in order to generate solutions to multiple attribute decision analysis problems.Asale place selection problem in Qingyang County of Chizhou in Anhui province of China is analysed using the proposed regression model to demonstrate its detailed implementation process,validity and applicability.