000			01964nam a22002057a 4500
999			_c4128 _d4128
005			20221014145052.0
008			221014b ||||| |||| 00| 0 eng d
020			_a9789811521461
082			_a620.028563 _bLEE
100			_aLee, Jay _99329
245			_aIndustrial AI: _b applications with sustainable performance
260			_bSpringer _aSwitzerland _c2020
300			_axx, 162 p.
365			_aEUR _b149.99
520			_aThis book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in infancy stage and may encompass the collective use of technologies such as Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects and the physical environment interact with each other to reach intended goals. A broad range of Industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further use that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remains useful life and further to optimize maintenance tasks to avoid disruption to operations.
650			_aArtificial intelligence--Industrial applications _97859
650			_aManagement _9445
650			_aIndustrial management _9210
942			_2ddc _cBK