IS200TDBTH6A Gas turbine system Mark VI

IS200TDBTH6A Product Introduction

(5) Perform predictive maintenance, analyze machine operating conditions, determine the main causes of failures, and predict component failures to avoid unplanned downtime.Traditional quality improvement programs include Six Sigma, Deming Cycle, Total Quality Management (TQM), and Dorian Scheinin’s Statistical Engineering (SE) [6]. Methods developed in the 1980s and 1990s are typically applied to small amounts of data and find univariate relationships between participating factors. The use of the MapReduce paradigm to simplify data processing in large data sets and its further development have led to the mainstream proliferation of big data analytics [7]. Along with the development of machine learning technology, the development of big data analytics has provided a series of new tools that can be applied to manufacturing analysis. These capabilities include the ability to analyze gigabytes of data in batch and streaming modes, the ability to find complex multivariate nonlinear relationships among many variables, and machine learning algorithms that separate causation from correlation.Millions of parts are produced on production lines, and data on thousands of process and quality measurements are collected for them, which is important for improving quality and reducing costs. Design of experiments (DoE), which repeatedly explores thousands of causes through controlled experiments, is often too time-consuming and costly. Manufacturing experts rely on their domain knowledge to detect key factors that may affect quality and then run DoEs based on these factors. Advances in big data analytics and machine learning enable the detection of critical factors that effectively impact quality and yield. This, combined with domain knowledge, enables rapid detection of root causes of failures. However, there are some unique data science challenges in manufacturing.(1) Unequal costs of false alarms and false negatives. When calculating accuracy, it must be recognized that false alarms and false negatives may have unequal costs. Suppose a false negative is a bad part/instance that was wrongly predicted to be good. Additionally, assume that a false alarm is a good part that was incorrectly predicted as bad. Assuming further that the parts produced are safety critical, incorrectly predicting that bad parts are good (false negatives) can put human lives at risk. Therefore, false negatives can be much more costly than false alarms. This trade-off needs to be considered when translating business goals into technical goals and candidate evaluation methods.

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IS200TRPGH3B I/O excitation redundant module GE
IS215ACLEH1B GE power control board
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IS200TGENH1A Gas turbine system Mark VI
DS200SDCCG5AHD GE power control board
IS200EROCH1AD I/O excitation redundant module GE
IS420UCECH1B From General Electric in the United States
IS220PAICH1A From General Electric in the United States
IS415UCVHH1A From General Electric in the United States
IS200WETAH1ADC High performance processor module GE
DS200ACNAG1ADD From General Electric in the United States
IS200DSFCG1AEB From General Electric in the United States
IS210AEBIH1BED GE power control board
IS220PSVOH1B Processor/Controller Mark VI System
IS200VCCCH1B From General Electric in the United States
IS200EGPAG1B I/O excitation redundant module GE
IS215UCVGH1A I/O excitation redundant module GE
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IS200TDBTH6ACD Processor/Controller Mark VI System
IS200DVIBH1B I/O excitation redundant module GE
IS420UCSCS2A I/O excitation redundant module GE
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DS200TBCAG1AAB High performance processor module GE
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IS215VCMIH2BE High performance processor module GE
IS215VCMIH2C I/O excitation redundant module GE
DS200TCDAH1BJE GE power control board
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IS420ESWBH3A High performance processor module GE
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IS420UCSCS2A-B GE power control board
IS220PDIOH1A GE power control board
DS200FCGDH1B High performance processor module GE
IS220PAICH1 From General Electric in the United States
IS200TRPGH1B From General Electric in the United States
IS200ACLEH1BCB GE power control board
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IS420ESWBH2A GE power control board
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IS200TDBTH6ACD High performance processor module GE
IS200DSPXH2CAA Gas turbine system Mark VI
DS200VPBLG2A From General Electric in the United States
IS200TVIBH2BBB Processor/Controller Mark VI System
IS200TSVOH1BCC Processor/Controller Mark VI System
IS200VAOCH1B GE power control board
IS200EPSMG1AED From General Electric in the United States
IS200EPCTG1A GE power control board
IS200VRTDH1D GE power control board
IS215UCVEM06A GE power control board
DS200DCFBG1BGB I/O excitation redundant module GE
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DS200SDCCG4AFD I/O excitation redundant module GE
DS200RTBAG3AHC Processor/Controller Mark VI System
IS200AEAAH1CPR1 I/O excitation redundant module GE
IS220UCSAH1A Processor/Controller Mark VI System
IS410STAIS2A High performance processor module GE
IS420UCSCH1B GE power control board
IS200AVIFH1A GE power control board
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IS200STCIH6A GE power control board
IS210AEBIH1A GE power control board
IS200ICBDH1A GE power control board