Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

Applications of machine learning to machine fault diagnosis: A review and roadmap

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Deep learning for smart manufacturing: Methods and applications

This paper presents an approach to implement vibration, pressure, and current signals for fault diagnosis of the valves in reciprocating compressors. Due to the complexity of structure and motion of such compressor, the acquired vibration signal normally involves transient impacts and noise. This causes the useful information to be corrupted and difficulty in accurately diagnosing the faults with traditional methods. To reveal the fault patterns contained in this signal, the Teager-Kaiser energy operation (TKEO) is proposed to estimate the amplitude envelopes. In case of pressure and current, the random noise is removed by using a denoising method based on wavelet transform. Subsequently, statistical measures are extracted from all signals to represent the characteristics of the valve conditions. In order to classify the faults of compressor valves, a new type of learning architecture for deep generative model called deep belief networks (DBNs) is applied. DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines (RBMs) and works through a greedy layer-by-layer learning algorithm. In pattern recognition research areas, DBN has proved to be very effective and provided with high performance for binary values. However, for implementing DBN to fault diagnosis where most of signals are real-valued, RBM with Bernoulli hidden units and Gaussian visible units is considered in this study. The proposed approach is validated with the signals from a two-stage reciprocating air compressor under different valve conditions. To confirm the superiority of DBN in fault classification, its performance is compared with that of relevant vector machine and back propagation neuron networks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.

An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks

Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART-ANFIS model has potential for fault diagnosis of induction motors.

http://eprints.hud.ac.uk/16575/1/(2009%2DESWA)_Fault_diagnosis_using_ANFIS.pdf

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

Machine performance degradation assessment and remaining useful life (RUL) prediction are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability. They provide a potent tool for operators in decision-making by specifying the present machine state and estimating the remaining time. For this ultimate purpose, a three-stage method for assessing the machine health degradation and forecasting the RUL is proposed. In the first stage, only the normal operating condition of machine is used to create identification model for recognizing the dynamic system behavior. Degradation index which is used for indicating the machine degradation is subsequently created based on the root mean square of residual errors. These errors are the difference between identification model and behavior of system. In the second stage, the Cox’s proportional hazard model is generated to estimate the survival function of the system. In the last stage, support vector machine, which is one of the remarkable machine learning techniques, in association with time-series techniques is utilized to forecast the RUL. The data of low methane compressor acquired from condition monitoring routine is used for validating the proposed method. The result shows that the proposed method could be used as a reliable tool to machine prognostics.

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines' operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

Van Tung Tran

Papers

An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks

Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems