Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance: From Data to Process Insights

Коротко и по делу о том, что важно знать про techniques.

The book covers a broad spectrum of techniques ranging from univariate control charts to deep learning-based prediction of remaining useful life. This book is designed to help readers quickly gain a working knowledge of machine learning-based techniques that are widely employed for building equipment condition monitoring, plantwide monitoring, and predictive maintenance solutions in process industry. Consequently, the readers can leverage the concepts learned to build advanced solutions for fault detection, fault diagnosis, and fault prognosis. Upon completion, readers will be able to confidently navigate the Prognostics and Health Management literature and make judicious selection of modeling approaches suitable for their problems. The application focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers and data scientists.

Part 1 lays down the basic foundations of ML-assisted process and equipment condition monitoring, and predictive maintenance. This book has been divided into seven parts. Part 2 provides in-detail presentation of classical ML techniques for univariate signal monitoring. Part 3 is focused on the widely popular multivariate statistical process monitoring (MSPM) techniques. Different types of control charts and time-series pattern matching methodologies are discussed. Emphasis is paid to both the fault detection and fault isolation/diagnosis aspects. These techniques come in handy for processes that cannot be satisfactorily handled via MSPM techniques. Part 4 covers the process monitoring applications of classical machine learning techniques such as k-NN, isolation forests, support vector machines, etc. Part 5 navigates the world of artificial neural networks (ANN) and studies the different ANN structures that are commonly employed for fault detection and diagnosis in process industry. Part 6 focusses on vibration-based monitoring of rotating machinery and Part 7 deals with prognostic techniques for predictive maintenance applications.

Broadly, the book covers the following:Exploratory analysis of process data

Best practices for process monitoring and predictive maintenance solutions

Univariate monitoring via control charts and time series data mining

Multivariate statistical process monitoring techniques (PCA, PLS, FDA, etc.)

Machine learning and deep learning techniques to handle dynamic, nonlinear, and multimodal processes

Fault detection and diagnosis of rotating machinery using vibration data

Remaining useful life predictions for predictive maintenance

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