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Intelligent Condition Based Monitoring

Part of the Studies in Systems, Decision and Control Book Series (volume 256)

DOI: 10.1007/978-981-15-0512-6

ISBN: 978-981-15-0511-9

Publisher: Springer Singapore - Springer Journals

Jan 14, 2020

1 - 9 of 9 Chapters

LitStream

Chapter 1Introduction

Verma, Nishchal K.; Salour, Al

Pages: 1 –5

[Intelligent condition based monitoring for early recognition of faults saves the industry from heavy losses occurring due to breakdowns. It is essential to prevent the machine breakdown as it may have a significant impact on production capacity and safety. Recognition of faults is necessary to...
LitStream

Chapter 2Faults and Data Acquisition

Verma, Nishchal K.; Salour, Al

Pages: 7 –88

[This chapter provides detail about the data acquisition process. It is the foremost step of a fault diagnosis framework where machine characteristics are measured and recorded for further analysis. The chapter starts with an introduction of air compressor, its parts, working principle, and...
LitStream

Chapter 3Pre-processing

Verma, Nishchal K.; Salour, Al

Pages: 89 –120

[As mentioned in Chap. 1, the real-time machine data is collected, which may be corrupt or inconsistent due to the presence of environmental noise. Therefore, the cleaning of data is required to remove unwanted frequencies as well to reduce the size of data for further analysis. This chapter...
LitStream

Chapter 4Feature Extraction

Verma, Nishchal K.; Salour, Al

Pages: 121 –173

[As detailed in Chap. 3, pre-processing of data improves the quality of data. Now, this chapter proceeds towards the next step of fault diagnosis framework where key characteristics of the data are found. For this purpose, data is analyzed in different domains and thus we obtain a new set of data...
LitStream

Chapter 5Feature Selection

Verma, Nishchal K.; Salour, Al

Pages: 175 –200

[ As detailed in Chap. 4, features have been extracted from the pre-processed data. Too many features may lead to the curse of dimensionality issues. This chapter explains how to obtain a set of relevant features which is the process of feature selection. Feature selection is the process in...
LitStream

Chapter 6Fault Recognition

Verma, Nishchal K.; Salour, Al

Pages: 201 –224

[This chapter details the last module of the fault diagnosis framework, i.e., fault recognition. After the collection of relevant subsets of the features, as detailed in the Chap. 5, classification is performed to decide the machine state. Chapter 2 provides details about different kinds of...
LitStream

Chapter 7Fault Diagnosis System for Air Compressor Using Palmtop

Verma, Nishchal K.; Salour, Al

Pages: 225 –266

[This chapter provides details of health monitoring of the air compressor using Palmtop. Until now, it has been observed that the data acquisition and fault recognition systems both are separately handled. To reduce the complexity and combine both processes into one system, Palmtop is used. The...
LitStream

Chapter 8Improved Fault Detection Model

Verma, Nishchal K.; Salour, Al

Pages: 267 –281

[ This chapter details about the improvement in different modules of the fault diagnosis framework. During data acquisition, the data is acquired in cyclic order instead of capturing in one shot. For feature extraction purpose, a new set of 343 features were introduced for detailed analysis....
LitStream

Chapter 9Fault Diagnosis System Using Smartphone

Verma, Nishchal K.; Salour, Al

Pages: 283 –302

[This chapter presents an Android application of a general-purpose fault diagnosis system developed earlier for desktop and Palmtop. The basic methodology of fault recognition using Android is similar to the methods discussed in Chap. 8. This chapter first describes the data mining model used for...

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