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International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 4 – Nov 2014 Computational Intelligence, Condition Monitoring and Optimization in Mechanical System- A Review M. Prem Kumar#1, K. Ganesan*2 # * PG Student,Department of Mechanical Engineering, Nandha Engineering College, Erode-52. India Assistant Professor, Department of Mechanical Engineering, Nandha Engineering College, Erode-52. India Abstract Energy is considered as the essential part for the national development in the form of mechanical power or any other which has major contribution for improving the quality of life and enhancing the economic growth. Thus, the use of wind turbine in the form of a renewable energy has become as one of the most viable alternative resource of power generation due to some compensations of it such as cost-effective and ecofriendly. In condition monitoring system it is difficult to implementation due to many uncertain parameters. For that uncertain parameters the computational intelligence technique is implemented like Neuro fuzzy, Fuzzy logic and FMECA. For that technique optimization is performed. And literature review is conducted for condition monitoring, computational intelligence and optimization. Keywords: Condition Monitoring, FMEA, FMECA, Neuro fuzzy. Introduction The goal of fuzzy controllers is to mimic a human operator’s actions or to make humanlike decisions by using the knowledge about controlling a target system (without knowing its model). This is achieved with fuzzy rules that constitute a fuzzy rule base. The fuzzy rule base is a central component of the fuzzy controller and it represents the “intelligence” in any fuzzy control algorithm. This is the place where the designer’s knowledge and experience must be correctly interpreted and organized into an appropriate set of rules.(Fuzzy rule) Let A and B be either fuzzy relations or fuzzy propositions. Then the structure [FR: IF A THEN B] is called a fuzzy rule. Every fuzzy rule can be divided into an antecedent part (IF . . .) and a consequent part (THEN . . .), with antecedent parts describing causes and consequent parts describing consequences relevant for control action. Such a form of fuzzy rules enables nonlinear mapping of inputs and outputs and thus enables creation of versatile static nonlinear control functions. The nonlinear character of these functions allows the fuzzy logic controller to cope successfully with complex nonlinear control problems. ISSN: 2231-5381 The size of the fuzzy rule base depends on the number of fuzzy rules, while the number of fuzzy rules depends on the number of input and output variables and on the number of linguistic values (fuzzy sets) associated with each of the variables. In general, the formation of fuzzy rules must follow some common sense in order to preserve basic fuzzy rule base characteristics such as consistency (contradiction), continuity, and completeness [1]. A CMS is a tool to ensure and measure the reliability of several running system. Evaluation of CMS is carried out by watching how efficiently it conveys the signal/alarm concerning an impending fault/crack. Condition Monitoring System is a method to realize the basic ideas of reliability theory. Neuro fuzzy logic An integrated control model using an adaptive neuro-fuzzy inference system for wind turbine power management strategy has been introduced. The vertical axis wind turbine is modeled for analysis. An artificial neural network is employed in this model to develop the fuzzy expert system to achieve a more realistic evaluation of wind power http://www.ijettjournal.org Page 163 International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 4 – Nov 2014 extraction. Experiments and Simulation have been carried out to investigate the effect of control strategy parameters of the wind turbine and its power extraction. The mathematical model has been formulated by understanding the wind nature, analyzing the mechanics of wind turbine blade interaction and the interaction of air-flowaerodynamics. For simulating and optimizing the design parameters the mathematical model is used for the system performance and wind power generation. Two kinds of air-flow model have been employed in simulating the aerodynamics of the wind turbine. One is the overall flow model, and the other is a local flow model. The overall flow model is based on a double multiple-stream tube model, including flow expansion in the lateral direction. The blade tip effects are significantly important in this model in order to use Prandtl lifting theory. The overall flow model is well suited for design investigation due to its reliable output results. The local flow model is based on one blade section conformal recording placed into a circle. The analytical model is employed with input data in terms of lift force and pitching moment [2]. A fuzzy scheme for failure mode screening In reliability centered maintenance (RCM), it is necessary to evaluate all the functional interrelationships among the system’s elements to assess the overall effects of the different component failure modes. In large system the nature of the dependencies may be unknown. Thus, fuzzy set operators, such as disjunction and conjunction operators, may denote a natural and simple way of representing the possibility of interdependencies among machines. It should be noted that these fuzzy operators need to be carefully chosen or formulated so that the equipment interdependencies under scrutiny can be properly rejected in the analysis. Fuzzy logic has also been considered for manipulating the morphological terms that an analyst employs in performing a failure modes, effects and criticality analysis (FMECA). The Linguistic variables can be adopted to describe the frequency of occurrence, severity, and detect capability of failure modes. Each one of such linguistic terms can be represented by fuzzy ISSN: 2231-5381 trapezoidal numbers. Through a defuzzification method or procedure, a conclusive linguistic variable (i.e., moderate, important, very important, etc.) can be resolved to determine the risk of each component failure mode. A fuzzy reasoning algorithm was developed and implemented via an expert system to evaluate and assess the likelihood of equipment failure mode and augmentation. The scheme is based upon the fuzzification of the effects of precipitating factors annoying the failure. It consists of a fuzzy mathematical formulation which linearly relates the presence of factors catalogued as important, critical or related to the occurrence of machine failure modes. The fuzzy algorithm was created to enable the implication mechanism of a constructed knowledge-based system to screen industrial equipment failures according to their likelihood of occurrence[3]. Failure Detection Depends on Fuzzy Inference System The devices used for protection and operators have responsibilities for the failure detection in a power system. The alarms are used for the protection devices and they are displayed to the operator by SCADA to respond to the respective appropriate and timely actions to be done. This paper is a part of a risk assessment study on the alarm of power system control centers using the Failure Modes and Effects Analysis (FMEA) that seeks to assess the failure detection in power system. The effective factors as fuzzy variables are used to evaluate them by use of fuzzy inference system. Advantages of the proposed fuzzy detection score are involving human reliability factors in addition to relay performance and modifying detection rate. The risk assessment depends on three variables: Failure occurrence (O), Severity of consequences(S) and detection of failures (D). This paper mainly focused on detection variable and prepared a specific model for the failure detection in a control center. Detection probability (D) means probability to detect the failure mode/causes. For detection of probability in FMEA are based on two definitions. First one is probability that the failure to be detected earlier to a customer. Second one is probability that the failure to be detected by a customer earlier to a disaster. At the beginning, the http://www.ijettjournal.org Page 164 International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 4 – Nov 2014 input data should be fuzzified and their membership degree to be identified. For this purpose, the triangular membership function is used. This paper represents a method based on fuzzy inference system to assess finding variable. At first the factors that influence failure mode detection are identified. Then a model is suggested based on a fuzzy inference system. The detection rate is modified and relay performance is calculated using the Bayes theory. The model validation is reviewed from two points of views. Finally, an illustrative example is presented. The model strengths include: To evaluate the detection variable, the ‘alarm status’ and ‘relay performance’ is considered. The ‘alarm status’ affects operator failure rate. Therefore, we have combined operator failure factors in control center with relay performance. This is a new work in risk assessment and failure detection assessment for a control center. They have identified factors that influence the failure detection and provided a model to determine the score. To determine the relay performance score, signal detection theory concept and Bayes rule are applied. This method has changed classic view of FMEA to detection score and false alarm is hitted, Miss and Correct rejection probabilities are calculated [4]. Fuzzy controller for DFIG A maximum power point tracking method is developed for the DFIG wind turbine system. To control rotor side converter the fuzzy controller is used to capture the maximum wind energy without measuring the wind velocity. To validate the proposed control strategy for 10KW DFIG wind generator the MATLAB /SIMULINK is used. This generator allows the production of electricity has variable speed, it allows them to better exploit the wind resources for different wind velocities. Underneath optimal control conditions, the variable speed wind system can extract a maximum wind power for a wide range of wind. A DFIG consists of a wound rotor induction generator with the stator windings directly connected to a three-phase power grid and with the rotor winding mounted to a bidirectional back-to-back IGBT frequency converter.Rotor side converter RSC and the grid side converter GSC are in 20-30% apparent power size. Controller of the utility side converter regulates the voltage across the DC link for power transmission to the gird; The RSC regulates the ISSN: 2231-5381 electro-magnetic torque or active power and it supplies some of the reactive power. A fuzzy logic controller is proposed to control the speed of the DFIG in order to track the maximum power operating point for a wide range of wind speed. The fuzzy logic controller is applied to control the rotor side converter (RSC) by using a stator flux oriented strategy. And an optimal speed position is estimated from the wind speed. At a given wind speed, the maximum energy obtained at an optimal TSR (tip speed ratio). Therefore as wind speed changes the turbine rotor speed needs to vary accordingly in order to maintain the tip speed ratio. The fuzzy controller (FLC) is mainly used in nonlinear modeled and have more inputs, uncertain factors and inaccurate properties. But in this case the fuzzy controller includes four parts: reasoning and defuzzification, fuzzification, fuzzy rule base. They have presented wind energy conversion system made with a doubly fed induction generator. A DFIG with the stator is connected directly to the grid while the rotor is connected to the grid through AC-DC-AC PWM converters. Based on a fuzzy logic controller (FLC) the rotor side converter is controlled. For controlling the RSC is to extract a maximum power from the wind without capturing his speed. The capability of the proposed controller is verified by using MATLAB/SIMULINK [5]. DFIG using Fuzzy-PI Control HAMANE et.al studied study analysis of a wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) connected to the electric power grid. The objective of the work is to apply and compare the dynamic performances of two types of controllers (PI and Fuzzy-PI) for the WECS. In terms of tracking and robustness WECS with respect to the wind fluctuation as well as the impact on the quality of the energy produced. The vector control with stator flux orientation of the DFIG is also utilized to control the active and reactive powers between the stator and the grid. Doubly-fed induction machine is an electrical three-phase asynchronous machine with wound rotor accessible for control. The power handled by the rotor side (slip power) is proportional to the slip; the energy required for rotor-side power converter which it handles only a small fraction of the overall system power. PI http://www.ijettjournal.org Page 165 International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 4 – Nov 2014 parameters are adjusted by fuzzy controller and it generates new parameters so that it fits all operating conditions, based on the error and its derivative. To analyze the system and compare efficiently the two proposed controllers, a set of simulation tests have been performed for 0.1sec, using Matlab–Simulink. In this paper, a decoupling control method of active and reactive powers for DFIG is developed. The appropriate model and vector control strategy have been established. There are two types of controllers are used as classical PI and Fuzzy-PI are synthesized to perform powers reference tracking and efficient disturbance dismissal. The results obtained that with the FuzzyPI controller is that the settling time is reduced considerably, peak overshoot of values are limited and oscillations are damped out faster compared to the conventional PI Controller. The transient response provided by the Fuzzy-PI Controller has been superior to the classical PI controller [6]. Fault Identification in Pitch-controlled System LIU Hao et.al developed a new method to diagnose faults in pitch-controlled system of wind turbine.The fuzzy theory is used to solve uncertainty inference problem through the establishment of the fault tree.The fault diagnosis research in wind turbines from abroad mainly focus on induction motor and gearbox.These methods have been successfully applied for automated fault diagnosis and have achieved well commercial result in practical applications. The number of faults occur in pitch-controlled system of wind turbine just followed the electrical system, and it also account for a large proportions of the longest machine halt time in parts and single failure longest downtime.It is difficult to be designated between fault mechanism and fault causes by mathematical models in the pitch-controlled system due to complexity and uncertainty or this, the rules of fuzzy fault diagnosis are presented (i) aimed at features of equipment fault in pitch-controlled system of wind turbine, combining the actual performance of wind turbine generating units and the ‘spot workers' and the ‘experts' experiences.(ii) based on the fuzzy reasoning, are formed. The purpose is to identify the fault cause based on failure indications, and give some quantitative positions to the maintenance staffs. ISSN: 2231-5381 The essence of beginning fault mechanism intelligent inference is to diagnosis the failure of pitch system by drawing support from the expert’s experiences and take a certain search strategy combines with fuzzy inference. The inference is divided into precise inference and fuzzy inference.In precise inference the fault location is determined through signs of phenomena observed, and then slightpossibility. According to some typical diagnosis till the final failure is identified.Rule-based FT is used to carry on precise inference in the intelligent fuzzy system. In Fuzzy inference the relationship between symptoms and fault reasons is designated by fuzzy fault diagnosis method which uses membership functions and fuzzy relation matrix in fuzzy set theory and it obtains membership functions of symptoms through membership functions of fault reasons.A fault diagnosis in pitch-controlled system of wind turbine using fuzzy interference with fault tree is developed. The membership and fuzzy rules, with collected operating data and fault information of wind turbine in wind farm, by trying neural network method, can be determined so that the dependence of experience and perception can be reduced [7]. Comparison of Sugeno-Type and MamdaniType Fuzzy Inference Systems ArshdeepKauret.aldeveloped a Fuzzy inference system for air conditioning system using Mamdani-type and Sugeno-type fuzzy models.Classical control theory is based on the mathematical models. It describes the physical plant under consideration. Fuzzycontrol is used to build a model of human expert who is capable of controlling the plant without thinking in terms of mathematical model.Fuzzy logic control was superior to traditional control. To set correct rules and determining the essence and range of fuzzy variables is time consuming work.Mamdani method is widely accepted for capturing expert knowledge it allows us to describe the expertise in more intuitive, more human-like manner. Sugeno method is computationally efficient and works well with based on adaptive techniques and optimization, it makes very effective in control problems, predominantly for dynamic non- linear systems.In Mamdani method it consists of two inputs from temperature and humidity sensors providing the temperature and humidity of the http://www.ijettjournal.org Page 166 International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 4 – Nov 2014 room. To controls the compressor speed the system has one output. The temperature and humidity are taken to be in ranges of 0ºC to 45ºC and 0% to 100% respectively. For each inputs four triangular membership functions has been used. ForSugenotype model, the initial steps are same as Mamdanitype model. The output compressor speed can only be either linear or constant in this FIS. The range of output in Sugeno-type FIS can only be 0-1.The results obtained for an air conditioning system Mamdani-type FIS and Sugeno-type FIS works similarly.Sugeno-type FIS air conditioning system works up to its full capacity whereas in Mamdanitype FIS it does not work upto full capacity.Sugeno-type FIS has an advantage that it can integrated with neural networks and genetic algorithm or other optimization techniques so that the controller can adapt to individual user, environment and weather [8]. Critically AssessmentFor Using Fuzzy Logic FMECA Analysis Failure Mode and Effects Analysis (FMEA) is a structured, qualitative analysis of a system to identify potential system failure modes, their effects and the causes on the system operation. FMECA extends this analysis by including an evaluation of the failure criticality. Due to fuzzy mathematicsit provides a tool for directly manipulating with the linguistic terms. And it also used in calculation RPN number. A criticality assessment based on fuzzy logic allows analysts to evaluate the risk associated with the item failure modes in a natural and easy way.The FMEA is a systematic approach for determining and evaluating each system, subsystem, part and component historical failure modes. This phase of the analysis is called the failure mode portion FMEA. If once the failure modes have been defined for the system, the potential effects, or impact on each part of system are evaluated conferring to the mission system safety.FMECA is often called as a safety analysis; its main benefit is that the systems designers learn more about the system while provide the analysis. FMECA should be done iteratively as the design develops.A FMECA is potentially one of the most beneficial analyses done in a well reliability program. FMECA is also one of the most tedious, time consuming, errors prone and difficult in development of a product. In future a computerized ISSN: 2231-5381 assistance needs to be developed to help in performing the analysis.. Fuzzy logic appears to be a powerful tool for performing a criticality analysis on a system design. And prioritizing failure identified in a FMEA for corrective actions. Fuzzy logic successfully combined both the quantitative factors that affect the riskiness of a design. In general, it provides an effective tool for representing and manipulating linguistic knowledge of the type used in a FMECA [9]. Exposure of stator winding fault in induction motor using fuzzy logic Pedro Vicente JoverRodrı´guez and Antero Arkkio presented a reliable method for the detection of stator winding faults (which make up 38% of induction motor failures) based on monitoring the line/terminal current amplitudes. The online monitoring of induction motors is flattering increasingly important. Difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. To diagnose induction motor faults a fuzzy logic approach may support. Fuzzy logic is used to make decisions about the stator motor condition in this method. The fuzzy system is based on knowledge expressed in rules and membership functions, which designate the behaviour of the stator winding. The finite element method (FEM) is utilized to generate virtual data that support the construction of the membership functions and give the probability to online test of the proposed system. Layout has been implemented in MATLAB-SIMULINK; with both data from a FEM motor simulation program and real measurements. In proposed method it is simple and it has the ability to work with variable speed drives. Fuzzy system is able to identify the motor stator condition with high accuracy. Three-phase induction motors are the ‘‘workhorses’’ of industry and it is most widely used in electrical machine. As of its simple structure and high reliability, induction motor is used for many purposes such as: blowers, pumps, fans, compressors, transportation, etc. In an industrialized nation, they can consume between 40 and 50% of total generated capacity of that country. Early detection of abnormalities in the motor will help to avoid expensive failures. To reduce unexpected failures and downtime the modern industry has widely used reliability-based and condition-based maintenance strategies. This paper expressed the feasibility of spotting stator http://www.ijettjournal.org Page 167 International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 4 – Nov 2014 failures in an induction motor by monitoring the motor current amplitudes using fuzzy logic. It is used in application of variable speed drives. Fuzzy logic system could be implemented in the software of the inverter to monitor the stator condition online. This paper presented the fusion between soft computing (fuzzy logic) and hard computing techniques (FEM) in order to design a reliable system. A possible drawback of the method is associated with the fact that a current imbalance originating from the supply source may be identified as a fault condition of the motor. But this shortcoming can be overcome by monitoring the voltage and introducing new rules in the inference system [10]. Risk estimation in failure mode and effects analysis Ying-Ming Wang et.al.examined the fuzzy risk priority numbers (FRPNs) are proposed for prioritization of failure modes. FRPNs are defined as fuzzy weighted geometric means of the fuzzy ratings for occurrence (O), Severity (S) and Detection (D), and can be computed using alphalevel sets and linear programming models. Intended for ranking purpose, the FRPNs are defuzzified using centroid defuzzification method, in which a new centroid defuzzification formula based on alpha-level sets is derived. Trapezoidal and triangular fuzzy numbers are defined for FMEA in linguistic terms. This paper examined a new fuzzy FMEA which allows the risk factors and their relative weights to be evaluated in a linguistic manner rather than in a defined way and a fuzzy RPN rather than a crusty RPN or fuzzy if–then rules to be defined for prioritization of failure modes. The proposed fuzzy FMEA provided a useful, effective, practical and flexible way for risk evaluation in FMEA [11]. Condition monitoring system (CMS) Adriaan Van Horenbeeket.al.examined that incondition monitoring system it is difficult to implementation during decision due to many uncertain parameters. In certainly the case for the wind turbine industry where factors like long logistical times and weather conditions have a major influence on the economic advantage. One of the parameters that are neglected in most of the existing literature is the performance of the ISSN: 2231-5381 condition monitoring system itself. This paper reveals a new concept for modeling this performance based on the P–F curve of different failure modes. A stochastic simulation model is constructed in order to quantify the economic added value of implementing a defectively performing condition monitoring system into a gearbox. A condition monitoring system (CMS) plays a critical role in beating the maximum potential of wind energy through wind turbine by minimizing the idle time. The cost of CMS design and installation is significant in comparison to other maintenance approaches but in the longer run CMS provides benefits exceeding the costs [12-15]. In fig.1 indicates the failure patterns that can be identified by condition monitoring and highlights the importance of the P–F curve and P–F interval. This curve envisions the drop in time of a particular component. While a component is operated, it will start to deteriorate until it entirely loses its ability to carry out its task. The point in time where the component undergoes critical failure is referred to as functional failure ‘F’. At that point the component can perform its regular task. The point in time where an indication of deterioration of the component can be detected is mentioned to as a potential failure ‘P’. Fig.1. P-F curve The expected life cycle cost of two maintenance strategies is determined and compared by a stochastic simulation model, that the performance of a CMS should be taken into account in order to draw the right conclusions on the real economic value [12]. Conclusion From the present review the following conclusion are drawn (i) an adaptive neuro-fuzzy inference system for wind turbine power management strategy has been introduced. And an artificial neural network is employed in this model to develop the fuzzy expert system to achieve a more realistic evaluation of wind power extraction. http://www.ijettjournal.org Page 168 International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 4 – Nov 2014 (ii) A fuzzy reasoning algorithm was developed and implemented via an expert system to evaluate and assess the likelihood of equipment failure mode and augmentation. The control of the rotor side converter based on a fuzzy logic controller (FLC) is presented. the Fuzzy-PI controller is that the settling time is reduced considerably, peak overshoot of values are limited and oscillations are damped out faster compared to the conventional PI Controller. A FMECA is potentially one of the most beneficial analyses done in a well reliability program. Fuzzy logic is used to make decisions about the stator motor condition. (iii) A new fuzzy FMEA which allows the risk factors and their relative weights to be evaluated in a linguistic manner rather than in a defined way and a fuzzy RPN rather than a crusty RPN or fuzzy if–then rules to be defined for prioritization of failure modes.(iv)Condition monitoring system for wind turbine is presented. 11. 12. 13. 14. 15. fuzzy logic”. Applied Soft Computing 8, pp. 1112–1120, 2008. Ying-Ming Wang a, Kwai-Sang Chin, Gary KaKwai Poon, Jian-Bo Yang, “Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean”. Expert Systems with Applications 36, pp. 1195–1207, 2009. Adriaan Van Horenbeek n, Joris Van Ostaeyen, Joost R. Duflou, LilianePintelon, “Quantifying the added value of an imperfectly performing condition monitoring system— Application to a wind turbine gearbox”. Reliability Engineering and System Safety, pp. 45-57, 2013. Z. Hameed, S.H. Ahn, Y.M. 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ArshdeepKaur, AmritKaur, “Comparison of MamdaniType and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System”. International Journal of Soft Computing and Engineering, Vol. 2, No. 2, pp. 323-325, 2012. Kováčová Monika, Jančo Roland, “Critically AssesmentForFmeca Analysis Using Fuzzy Logic”. Journal of Applied Mathematics, Vol. 1, No. 1, pp. 261270, 2008. Pedro Vicente JoverRodrı´guez , Antero Arkkio, “Detection of stator winding fault in induction motor using ISSN: 2231-5381 http://www.ijettjournal.org Page 169