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A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life. Prognostics and Health Management provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics. Written by noted experts in the field, Prognostics and Health Management clearly describes how to extract signatures from conditioned-based data using conditioning methods such as data fusion and transformation, domain transformation, data type transformation and indirect and differential comparison. This important resource: Integrates data collecting, mathematical modelling and reliability prediction in one volume Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes Presents information from a panel of experts on the topic Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods Written for system engineers working in critical process industries and automotive and aerospace designers, Prognostics and Health Management offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life.

This paper presents a comprehensive study on dynamic rotor balancing and vibration analysis as part of a predictive maintenance framework for thermal power plants, with a case study focused on the TVF-100-2 turbo generator. The methodology involves on-site multi-plane balancing under real operational conditions, supported by spectral vibration diagnostics, phase angle evaluation, and orbit analysis. These advanced techniques enable precise identification of unbalance-related vibration issues and their effective mitigation without disassembly. This study demonstrates how integrating dynamic balancing with continuous monitoring and diagnostic analysis enhances operational stability and extends equipment lifespan. The findings contribute to more efficient predictive maintenance strategies, with significant implications for reducing downtime and improving the reliability of rotating machinery in thermal power generation.

\"This book gives a complete presentation of the basic essentials of machinery prognostics and prognosis oriented maintenance management, and takes a look at the cutting-edge discipline of intelligent failure prognosis technologies for condition-based maintenance. Latest research results and application methods are introduced for signal processing, reliability moelling, deterioration evaluation, residual life prediction and maintenance-optimization as well as applications of these methods\"

A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life. Prognostics and Health Management provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics. Written by noted experts in the field, Prognostics and Health Management clearly describes how to extract signatures from conditioned-based data using conditioning methods such as data fusion and transformation, domain transformation, data type transformation and indirect and differential comparison. This important resource: Integrates data collecting, mathematical modelling and reliability prediction in one volume Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes Presents information from a panel of experts on the topic Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods Written for system engineers working in critical process industries and automotive and aerospace designers, Prognostics and Health Management offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life.

The reliability of gas turbines is crucial due to their critical applications in energy systems and the increasing complexity of their design and operation. Traditional failure mode and effects analysis (FMEA) methods face significant limitations in handling combined uncertainty under conditions of ambiguity and partial information. Although widely used, fuzzy variations of FMEA naturally fall short in simultaneously addressing both sources of uncertainty: Ambiguity and incomplete knowledge. This study investigates the application of fuzzy‐rough FMEA (FR‐FMEA) to bridge this gap. By integrating fuzzy logic with rough set theory, FR‐FMEA effectively manages uncertainties arising from incomplete knowledge and vagueness in expert judgment, providing a more reliable framework for risk prioritization. A case study on a gas turbine demonstrates the application of the proposed method. The results show that FR‐FMEA provides distinct and reliable rankings, reducing clustering while aligning more closely with conventional RPN rankings. Key components such as the combustion chamber, fuel nozzle, and turbine rotor were consistently identified as high‐risk across methods, emphasizing their criticality for maintenance and design optimization. Moreover, the results are also compared with conventional FMEA and Fuzzy‐FMEA to highlight the differences. FR‐FMEA combines fuzzy logic and rough set theory to address uncertainty and incomplete knowledge in failure analysis. Applied to a gas turbine system, it delivers more reliable and distinct risk rankings, identifying critical components for targeted maintenance and improved reliability.

Pointer plays a very important role in C/C++, but it has also become a nightmare for many programmers. In order to ensure that program will not be terminated because of any null pointer dereference error, most people will perform a null pointer check (such as ”if (ptr != NULL)”) to make sure the error never happen. But for programmers who write libraries, should they also do any null pointer checks on external pointer parameters? Or should they leave the responsibility to library users, the developers who use the library? In the absence of a unified standard, it is easy to check the same pointer redundantly, or even miss the necessary check and eventually cause serious vulnerability. Therefore, we want to analyze the timing of the use of the empty index check and the security impact it brings. The objects include open source code libraries and software. Library users can use our analysis results to ensure that there is no potential null indicator check missing and improve the security of the program.

This book has been written to provide both students and industrial man­ agers with a comprehensive description of the tools and techniques of Quality Management and also to provide a framework for understanding Quality Development. Central to the theme of this book is the idea that quality management is a developmental process which requires an understanding of the techniques, the people and the systems issues. The aims of quality development are to produce greater organizational consistency, to improve customer satisfac­ tion and to reduce the business process costs. In order to achieve these aims, managers are required to have an understanding of both the underlying the­ ories and the methodologies for implementation. The aim of this book is to provide a coherent description of both the theoretical and implementation aspects of quality management. Since the halcyon days of the quality 'revolution' of the 1970s and 1980s, many organizations have realized that quality development represents an enormous management challenge. This challenge for continuous improve­ ment requires the continuous development of systems, of techniques and of people. Like most serious business strategies, competitive improvement through quality development can only be achieved if the organization understands not only what the various quality 'options' are but also when a particular technique or approach is applicable. Quality development has no single blueprint but requires a learning organization which understands key concepts and methods of implementation.