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A MACHINE LEARNING-BASED ALGORITHM FOR PREDICTIVE MAINTENANCE IN AN INDUSTRY

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 Format: MS WORD ::   Chapters: 1-5 ::   Pages: 87 ::   Attributes: experimnent ::   341 people found this useful
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CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND OF THE STUDY

Predictive maintenance (PdM) or sometimes called “on-line monitoring”, “risk-based maintenance”, or “condition-based maintenance”, is a subject to many recent research papers with the long history behind it. It refers to the intelligent monitoring of equipment to avoid future failures. Predictive maintenance has evolved from the first method that is visual inspection to automated methods using the advanced signal processing techniques based on pattern recognition and machine learning, neural networks, fuzzy logic, etc. The automated methods provide viable solution to many industries detecting and collecting sensitive information from the equipment which are mainly motors, where human eyes or ears can cease to do so (H. M. Hashemian and W. C. Bean, 2011). Together with integrated sensors, predictive maintenance can avoid unnecessary equipment replacement, reduce machine downtime, find the root cause the fault, and in this way save costs and improve efficiency. Predictive maintenance overlaps with the scope of preventive maintenance in terms of scheduling the maintenance activity in advance to avoid machine failures. In contrast to conventional preventive maintenance, predictive maintenance schedules activities are based on collected data from sensors and analysis algorithms (S.-j. Wu, 2007). In process industries, induction motors make up approximately 70% of all driven electrical loads. In this regard, there has been much interest on ways to better diagnose the wellness condition of these motors. The bearing failure is identified as the most frequent cause of motor failure and most common maintenance problem. Accordingly, predictive maintenance mainly focuses on two aspects: energy efficiency improvement (key of energy saving) and unscheduled downtime reduction. The algorithms developed around these two can also be generally divided into two categories: 1) energy and efficiency, there have been many evaluation methods and devices developed, as in (B. Lu, T. G. Habetler, 2006), 2) system condition monitoring, including the detection of motor faults, with various fault-detection techniques developed, as for instance in (W. T. Thomson and M. Fenger, 2001). Some initial concepts of intelligent predictive decision support systems have been introduced in (R. Yam, P. Tse, 2001). Algorithms are essential for effective predictive maintenance. There are various kinds of techniques to be applied in various phases of PdM implementation, i.e. data processing, diagnostics, and prognostics, as given in (J.-H. Shin, 2015). In PdM three kinds of approach can be distinguished: 1) Data-driven approach; 2) Model-based approach; 3) Hybrid approach. The data-driven approach is also known as the data mining approach or the machine learning approach, which uses historical data to learn a model of system behavior. Model-based approach has the ability to incorporate physical understanding of the target product, relying on the analytical model to represent the behavior of the system.

1.2 STATEMENT OF THE PROBLEM

Machine learning approaches are viably used in the areas where the availability of data is increasing such as the maintenance in industry sector. It is increasingly providing effective solutions, cloud-based solutions, and newly introduced algorithms. Machine Learning-based PdM can be divided into the following two main classes: Supervised, where information on the occurrence of failures is present in the modelling dataset and Unsupervised - where logistic and/or process information is available, but no maintenance data exists. The availability of maintenance information mostly depends on the nature of the existing maintenance management policy. When possible, supervised solutions are preferable. From the Machine Learning perspective, depending on the output of the data set, two classes of supervised problem are possible: regression problem (if output assumes continuous values), and classification problem (if output assumes categorical values).

1.3 AIM AND OBJECTIVES OF THE STUDY

The study seeks to evaluate a machine learning-based algorithm for predictive maintenance in an industry. The objectives of the study are:

  1. To determine the nature and effectiveness of machine learning-based algorithm in predicting industrial maintenance
  2. To determine the types of machine learning-based algorithm
  3. To identify other types of maintenance strategies in an industry using machine learning-based algorithm
  4. To generate a time series model in the machine learning-based algorithm for predictive maintenance in an industry

1.4 SIGNIFICANCE OF THE STUDY

The following are the significance of this study:

1.     The outcome of this research will create awareness to both the general public and manufacturing industries about a machine learning-based algorithm for predictive maintenance in an industry.

2.     This research will be a contribution to the body of literature in the area of a machine learning-based algorithm for predictive maintenance in an industry, thereby constituting the empirical literature for future research in the subject area

1.5 THE SCOPE OF THE STUDY

The study covers on a machine learning-based algorithm for predictive maintenance in an industry

 1.6 DEFINITION OF TERMS

MACHINE LEARNING-BASED ALGORITHM: Machine learning algorithms are mathematical model mapping methods used to learn or uncover underlying patterns embedded in the data. Machine learning comprises a group of computational algorithms that can perform pattern recognition, classification, and prediction on data by learning from existing data (training set).

MAINTENANCE: The technical meaning of maintenance involves functional checks, servicing, repairing or replacing of necessary devices, equipment, machinery, building infrastructure, and supporting utilities in industrial, business, and residential installations

 

 

 

 

 


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Project Information

Format:MS WORD
Chapter:1-5
Pages:87
Attribute:experimnent
Price:₦3,000
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