Data-centric monitoring method
First Claim
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1. ) A data-centric monitoring method for detecting, isolating, and predicting abnormal conditions in a system comprising the steps of:
- a) acquiring measured data relating to at least one part or piece of the system;
b) filtering bad or corrupt data;
c) deriving a baseline for each monitored variable;
d) calculating a residual from the baseline for each data point;
e) calculating a trend line based on the residuals of each monitored variable;
f) detecting data points whose residuals fall outside a normal operating limit for each monitored variable;
g) detecting data points whose residuals violate one or several rules for abnormal conditions for each monitored variable;
h) detecting any rapidly changing trend line slope or shape;
i) issuing one or multiple alerts or warnings for any violations of the above; and
j) consolidating multiple alerts or warning that correspond to the same cause into a single alert or warning of fault or faults;
k) estimating the severity of each fault;
l) estimating the effect of each fault on each system capability;
m) analyzing each system capability variation of trend;
n) extrapolating data along the trend line;
o) detecting when data points will be outside operating limits;
p) detecting when data points will violate abnormal condition rules;
q) issuing one or multiple alerts or warnings for any detected violations of steps “
o” and
“
p”
; and
r) consolidating multiple alerts or warning that correspond to the same cause into a single alert or warning;
s) estimating fault severity and system capability over a future time window of interest.
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Accused Products
Abstract
Health management of machines and/or equipment, such as gas turbine engines, airplanes, and industrial equipment using a model centric method.
47 Citations
21 Claims
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1. ) A data-centric monitoring method for detecting, isolating, and predicting abnormal conditions in a system comprising the steps of:
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a) acquiring measured data relating to at least one part or piece of the system;
b) filtering bad or corrupt data;
c) deriving a baseline for each monitored variable;
d) calculating a residual from the baseline for each data point;
e) calculating a trend line based on the residuals of each monitored variable;
f) detecting data points whose residuals fall outside a normal operating limit for each monitored variable;
g) detecting data points whose residuals violate one or several rules for abnormal conditions for each monitored variable;
h) detecting any rapidly changing trend line slope or shape;
i) issuing one or multiple alerts or warnings for any violations of the above; and
j) consolidating multiple alerts or warning that correspond to the same cause into a single alert or warning of fault or faults;
k) estimating the severity of each fault;
l) estimating the effect of each fault on each system capability;
m) analyzing each system capability variation of trend;
n) extrapolating data along the trend line;
o) detecting when data points will be outside operating limits;
p) detecting when data points will violate abnormal condition rules;
q) issuing one or multiple alerts or warnings for any detected violations of steps “
o” and
“
p”
; and
r) consolidating multiple alerts or warning that correspond to the same cause into a single alert or warning;
s) estimating fault severity and system capability over a future time window of interest. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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Specification
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Current AssigneeIntel Corporation
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Original AssigneeScientific Monitoring, Inc.
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InventorsMink, George, Tran Van, Hoang, He, Yi-hua, Wang, Yu-tsung, Jaw, Link
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Granted Patent
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Time in Patent OfficeDays
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Field of Search
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US Class Current714/48
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CPC Class CodesG05B 23/024 Quantitative history assess...G05B 23/0262 Confirmation of fault detec...G05B 23/0278 Qualitative, e.g. if-then r...
