IS220YDOAS1AJ Processor/Controller Mark VI System

IS220YDOAS1AJ 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.

IS420UCSCS2A-B From General Electric in the United States
IS200TVBAH2ACC I/O excitation redundant module GE
IS200JPDSG1ACBGE From General Electric in the United States
IS220PDIIH1B GE power control board
DS200LDCCH1AGA GE power control board
IS200BLIGH1A Gas turbine system Mark VI
IS200ERRBG1A From General Electric in the United States
IS200DSPXH1D I/O excitation redundant module GE
IS200ERIOH1ACB From General Electric in the United States
IS200TBTCH1CBB High performance processor module GE
IS200DAMAG1B From General Electric in the United States
IS200VAICH1DAA From General Electric in the United States
IS200HFPAG2A I/O excitation redundant module GE
IS200RCSAG1ABB GE power control board
IS200TREGH1BDC Processor/Controller Mark VI System
IS210DTAIH1A Processor/Controller Mark VI System
IS230TNTRH1C Gas turbine system Mark VI
DS200TCEAG1BNE I/O excitation redundant module GE
IS200JPDDG1A Processor/Controller Mark VI System
IS200TRLYH1BGF GE power control board
DS200TCEAG1BTF High performance processor module GE
IS200TRLYH1C High performance processor module GE
IS210AEBIH1ADC From General Electric in the United States
IS200ESYSH3ABB Gas turbine system Mark VI
IS200HFPAG2ADC GE power control board
IS200BICRH1A GE power control board
IS220PRTDH1B Processor/Controller Mark VI System
DS200ACNAG1ADD High performance processor module GE
DS200TCRAG1ABB GE power control board
DS200SDCCG4AFD High performance processor module GE
DS200GDPAG1AKF High performance processor module GE
DS200TCCAG1BAA I/O excitation redundant module GE
DS215TCDAG1BZZ01A From General Electric in the United States
GDS1168-PFF-PA-NF Processor/Controller Mark VI System
IS210MACCH2AEG High performance processor module GE
IS200EGPAG1B Gas turbine system Mark VI
IS200SPROH1AAB Processor/Controller Mark VI System
DS200UPSAG1A Processor/Controller Mark VI System
DS200TBQCG1AAA High performance processor module GE
DS200SPCBG1ADC I/O excitation redundant module GE
DS200IIBDG1A High performance processor module GE
IS200TRLYS1BGG High performance processor module GE
IS215UCVFH2AB High performance processor module GE
DS200SDCCG5A GE power control board
IS210AEBIH3B Gas turbine system Mark VI
IS200DAMDG2A Processor/Controller Mark VI System
DS3820LT4AICIA From General Electric in the United States
IS400JPDHG1ABB Gas turbine system Mark VI
IS200VTCCH1CBC GE power control board
DS200TCDAH1B Processor/Controller Mark VI System
IS210AEACH1A I/O excitation redundant module GE
DS200LDCCH1AGA Processor/Controller Mark VI System
IS410TRLYS1F High performance processor module GE
IS220PRTDH1B High performance processor module GE
IS215SECAH1A I/O excitation redundant module GE
IS200TBTCH1C I/O excitation redundant module GE
DS200TCQCG1BKG Gas turbine system Mark VI
IS200BPVCG1B I/O excitation redundant module GE
IS210BPPBH2B From General Electric in the United States
IS200EPDMG1ABA I/O excitation redundant module GE
IS200STCIH6ADD Gas turbine system Mark VI
IS200VCRCH1BBB GE power control board
IS200BPIIH1AAA High performance processor module GE
IS420YDOAS1B Processor/Controller Mark VI System
IS210AEAAH1B GE power control board
DS200TCDAG1PR5A Processor/Controller Mark VI System
IS200DAMDG2AAA Processor/Controller Mark VI System
DS200SDCCG4AFD From General Electric in the United States
IS200ICBDH1ABB From General Electric in the United States
DS200ACNAG1ADD Processor/Controller Mark VI System