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1. 发明公开

EP3191905A1 GAS TURBINE FAILURE PREDICTION UTILIZING SUPERVISED LEARNING METHODOLOGIES 审中-公开
标题翻译：采用监督学习方法的燃气轮机故障预测
公开(公告)号：EP3191905A1
公开(公告)日：2017-07-19
申请号：EP15766688.4
申请日：2015-09-03
申请人： Siemens Energy, Inc.
发明人： CAI, Xinmin , CHAKRABORTY, Amit , EVANS, Matthew , GOH, Siong Thye , YUAN, Chao
IPC分类号： G05B23/02
CPC分类号： G06N5/047 , G05B23/0229 , G06N99/005
摘要： A system and method for predicting failures of machinery such as a gas turbine. The system and method utilizes computer-based system to annotate historical data locate a prior failure event. Data associated with sensor readings prior to the failure event is annotated to note that it is likely associated with a failure and is compared to normal operating condition data. A fast boxes algorithm is used to learn the location of the pre-event data (positive class, minority group) with respect to the normal operation data (negative class, majority group). An evaluation is performed to analyze the discriminatory strength of the pre-event data with respect to the normal data, and if a relatively strong difference is found, the associated pre-event data is stored and used as a “symptom” to monitor the on-going performance of a machine and predict the possibility of an unexpected failure days before it would otherwise occur.
摘要翻译：一种用于预测诸如燃气轮机的机器故障的系统和方法。该系统和方法利用基于计算机的系统来标注定位先前故障事件的历史数据。与故障事件之前的传感器读数相关联的数据被注释以指出它可能与故障相关并且与正常操作条件数据相比较。使用快速盒子算法来学习正常操作数据（负类别，多数组）的事件前数据（正类，少数群）的位置。进行评估以分析事件前数据相对于正常数据的判别强度，并且如果发现相对较强的差异，则将相关联的事件前数据存储并用作症状以监视正在进行的机器的性能，并预测在发生其他情况发生之前几天发生意外故障的可能性。

2. 发明授权

EP3191797B1 GAS TURBINE SENSOR FAILURE DETECTION UTILIZING A SPARSE CODING METHODOLOGY 有权
公开(公告)号：EP3191797B1
公开(公告)日：2018-08-01
申请号：EP15767628.9
申请日：2015-09-03
申请人： Siemens Energy, Inc.
发明人： GOH, Siong Thye , YUAN, Chao , CHAKRABORTY, Amit , EVANS, Matthew
IPC分类号： G01D3/08
CPC分类号： G01D3/08 , G01M15/14 , G05B23/024 , G06F17/18
摘要： A method and system for recognizing (and/or predicting) failures of sensors used in monitoring gas turbines applies a sparse coding process to collected sensor readings and defines the L-1 norm residuals from the sparse coding process as indicative of a potential sensor problem. Further evaluation of the group of residual sensor readings is perform to categorize the group and determine if there are significant outliers (“abnormal data”), which would be considered as more likely associated with a faulty sensor than noisy data. A time component is introduced into the evaluation that compares a current abnormal result with a set of prior results and making the faulty sensor determination if a significant number of prior readings also have an abnormal value. By taking the time component into consideration, the number of false positives is reduced.

3. 发明授权

EP3191905B1 GAS TURBINE FAILURE PREDICTION UTILIZING SUPERVISED LEARNING METHODOLOGIES 有权
公开(公告)号：EP3191905B1
公开(公告)日：2019-05-15
申请号：EP15766688.4
申请日：2015-09-03
申请人： Siemens Energy, Inc.
发明人： CAI, Xinmin , CHAKRABORTY, Amit , EVANS, Matthew , GOH, Siong Thye , YUAN, Chao
IPC分类号： G05B23/02

4. 发明公开

EP3191797A1 GAS TURBINE SENSOR FAILURE DETECTION UTILIZING A SPARSE CODING METHODOLOGY 审中-公开
标题翻译：采用稀疏编码方法的燃气轮机传感器故障检测
公开(公告)号：EP3191797A1
公开(公告)日：2017-07-19
申请号：EP15767628.9
申请日：2015-09-03
申请人： Siemens Energy, Inc.
发明人： GOH, Siong Thye , YUAN, Chao , CHAKRABORTY, Amit , EVANS, Matthew
IPC分类号： G01D3/08
CPC分类号： G01D3/08 , G05B23/024
摘要： A method and system for recognizing (and/or predicting) failures of sensors used in monitoring gas turbines applies a sparse coding process to collected sensor readings and defines the L-1 norm residuals from the sparse coding process as indicative of a potential sensor problem. Further evaluation of the group of residual sensor readings is perform to categorize the group and determine if there are significant outliers (abnormal data), which would be considered as more likely associated with a faulty sensor than noisy data. A time component is introduced into the evaluation that compares a current abnormal result with a set of prior results and making the faulty sensor determination if a significant number of prior readings also have an abnormal value. By taking the time component into consideration, the number of false positives is reduced.
摘要翻译：用于识别（和/或预测）用于监测燃气轮机的传感器的故障的方法和系统对收集的传感器读数应用稀疏编码过程，并且将来自稀疏编码过程的L-1范数残差定义为潜在传感器问题的指示。进一步评估残余传感器读数组，对组进行分类并确定是否存在显着的异常值（异常数据），与噪声数据相比，这可能被认为与故障传感器相关的可能性更大。将一个时间分量引入到评估中，将当前异常结果与一组先前结果进行比较，并且如果大量先前读数也具有异常值，则确定传感器故障。通过考虑时间分量，减少了误报的数量。

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