Predictive Maintenance Planning With Fuzzy Neural ... - AMMJ

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condition of equipment and their status in Which are under operation with signs of wear and possibleimpending failures, so that will be revealed TPM plays an important role in Predictive maintenance,involving collecting and interpreting online data on the production and operation of the majorcomponents of equipment. This will be more obvious in Failure prediction, and determination of PMappropriation in repair strategy. [ 4]Development of TPM initiated in1970, With the continuous expansion of knowledge and Continuoustechnical state of equipment TPM approach usually is known in the production parts and in this contextis known to be applied in medium and high volume This is probably due to implementation of TPMwhich In areas of high volume production is remarkable, and beneficially, so important for theinstitution. However, in contrast TPM Should not to be limited in high volume areas and to averagevolume, The approach should not be Only considered as to be used for machines manufacturing in theworkshop.It is the philosophy should be spread throughout the company's operational activities,Increasing the awareness and understanding of all people at all levels of the organization.In recent years, artificial neural networks have been used successfully in pattern recognition tasks andfault detection problems. One of the main advantages of using artificial neural networks is the ability todetect patterns that are inaccurate. This research seeks to implement the fuzzy neural network inpredictive maintenance in fault patterns.NEP et al. provided a real-time neural network position control system based on workflow ofMechanical equipment. In this study, another type of neural network Called ARTMAP Developed in1992 by a Carpenter and his colleagues used. In such a way, network training, is a combination ofsupervised Training and unsupervised learning. Also in this model, new information about the status ofthe machine is controlled by continuously oscillating Data. Javadpour and Knapp worked on Fuzzyneural networks approach in condition monitoring of machinery and inputs, they used thecharacteristics of fuzzy sets and the membership values for classification between zero and one. [ 13]In addition, In application of system maintenance by Jones and, Evans, Bansal the neural networkapproach is applied [ 4]. Bansal et al. used a Predictive real-time plant-based production, accompaniedwith learning characteristic of neural network mappings for non-Liner to allow recognition of machineparameters. By this the cost of the data parameter measuring was reduced. In their investigation, threegroups of training data, Data validation and test data models, were classified. Then data had beennormalized .To summarize the data, Principal Component Analysis (PCA) has being Used in theirwork, dimensions of Data Has decreased from 400 to 14. They utilized a multi-layer perceptionnetwork with combined optimization, in a graded trained soft-max activation function (ScaledConjugate Gradient-(SCG)).Mrossi, Mazzieri and Lucifredi identified the best technique for control systems, as two methods:multiple linear regression analysis and a developed technique called dynamic kriging were comparedwhile neural networks were being used in. The results of this research show inefficiency of statisticalmethods in arriving to an optimal solution. The combinations of these methods with neural networkhave been effective in optimal maintenance prediction time. [ 16]The mentioned network had 15 neurons in the first layer and combined of the 10 neurons for thesecond layer. Molina et al. combined neural network with expert system joined with preventivemaintenance (NNMP -Neural Network Maintenance Predictive). [ 18].They suggested a neural network approach as a very suitable one to have a predictive control system.The reason was on non-linear relationship between the data and the abnormal condition. In contrast, theexpert systems try to imitate operator response and analyze the same variables like humans. NeuralNetworks overcome the limitations and try linear regression analysis relations between the differentmarkings. In this study identification of ARTMAP has used different situation architecture in order toprevent unfavorable conditions in the future. [ 18]
In another trial, Bansal et al have devised an inversion algorithm to confirm current movements forestimating the parameters of the system. It was in a predictive maintenance system called Bjest. Thisalgorithm was based on neural networks, used the function mapping nonlinear control systems fortraining. Therefore, the quality of network depends on Data Performance in network training. [ 3]In recent years, creating a Combination model of fuzzy logic and neural networks for betterperformance basically, have made many attempts in the decision system. Developing literature onjoining Fuzzy system and neural networks together to expand the systems, in many papers oftenincludes the following: [ 11]-Using neural networks for adaption in Fuzzy systems;- The combination of fuzzy systems and neural networks that are called: "Hybrid Systems";- The application of neural networks to estimate Fuzzy Neural Network;Neural networks are simple computational tool for testing data and the model data structure. Data areused to create models known as the training data. While a neural network uses training data forlearning patterns in the data, they can achieve the outputs and different outcomes. Different types ofneural networks synthesized and according to the research objectives can be used, as one of the mostfamous is multilayered feed forward MFNN. [ 26]According to recent studies, multi-layer feed forward neural network has more than fifty percent ofapplied studies reported in Neural Network that have been utilized with laws of propagation learningalgorithm [ 3]. This type of neural network with wide applications in many aspects such as Managementprinciples, issues in prediction classification, and modeling. Solving problems that are considered“MFNN” involves learning the relations. A set of input and output are specified between. In fact, it is asupervised learning technique to learn about data relations among data set forms the trainings.Although artificial neural networks and fuzzy systems are different from each other in terms ofstructure, however, if they can be viewed by strengths and weaknesses, it is observed that the natures ofthe two systems are complementary to each other. Thus, the combination of fuzzy systems, artificialneural networks and learning capabilities of neural networks, will be entered into fuzzy systems.2. Fuzzy Neural NetworkLearning processes in fuzzy neural networks is the variation of parameters or coefficients of networkusing examples have already been experienced. This operation of the network parameters adaption isdone so that a defined index reaches a satisfying answer that the lowest or highest possible providingamount is fulfilled. This is required, having a set of ordered pairs of input and output, and also there isan indicator that expresses the mapping of inputs x k to the desired output values of t k .The structure of fuzzy neurons is like neurons in definite space, Except that all or some of thecomponents and parameters are expressed in terms of fuzzy logic. To convert a Conventional neuron tofuzzy neurons, there are several ways. Using each of them, result in a variety of fuzzy Neurons.In a fuzzy neuron Input vector X=⎣x1, x 2 ,..., x n⎦T ∈Rn is defined in [0,1]n space ,This vector isdefined, of fuzzy symptoms With a membership function in the range of (1, 0) .Inputs after makingchanges By coefficients, insert to the neuron as the pure inputs.The variation of coefficient by inputs may be different. This change can be ordinary multiplication orthe maximum related input rate. The concept of fuzzy neurons can be assumed that each neuron has afuzzy representation of a linguistic variable such as low, medium and so on. This means that the jthneuron output yj, reviews representing a membership function. Which shows the input vectormembership rate of ⎣ x1, x2,..., xn ⎦ r to linguistic categories belongs.
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