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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Data-Driven Fault Diagnosis for Complex Industrial Processes: Towards Fault Prediction, Detection and Identification

Data-Driven Fault Diagnosis for Complex Industrial Processes: Towards Fault Prediction, Detection and Identification

Yin, Hongpeng
Zhou, Han
Chai, Yi

Hardcover

Series: Engineering Applications of Computational Methods, Book 22

Technology & EngineeringGeneral Mathematics

Currently unavailable to order

ISBN10: 9819631521
ISBN13: 9789819631520
Publisher: Springer
Published: Apr 16 2025
Pages: 208
Weight: 1.17
Height: 0.61 Width: 6.50 Depth: 9.35
Language: English

This book summarizes techniques of fault prediction, detection, and identification, all included specifically in the data-driven fault diagnosis requirements within industrial processes, drawing from the combination of data science, machine learning, and domain-specific expertise. In the modern industrial processes, where efficiency, productivity, and safety stand as paramount pillars, the pursuit of fault diagnosis has become more crucial than ever. The widespread use of computer systems, along with new sensor hardware, generates significant quantities of real-time process data. It has been frequently asked what could be done with both the real-time and archived historical data, to not only promising efficiency but providing prospect of a brighter, more resilient future. This book starts with the definition, related work, and open test-bed for industrial process fault diagnosis. Then, it presents several data-driven methods on fault prediction, fault detection, and fault diagnosis, with consideration of properties of industrial processes, such as varying operation modes, non-Gaussian, nonlinearity. It distills cutting-edge methodologies and insights which may inspire for industrial practitioners, researchers, and academicians alike.

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