Invention Grant
- Patent Title: Fault diagnosis employing probabilistic models and statistical learning
- Patent Title (中): 使用概率模型和统计学习的故障诊断
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Application No.: US12694651Application Date: 2010-01-27
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Publication No.: US08694836B2Publication Date: 2014-04-08
- Inventor: George Lapiotis , David Shallcross
- Applicant: George Lapiotis , David Shallcross
- Applicant Address: US NJ Piscataway
- Assignee: Telcordia Technologies, Inc.
- Current Assignee: Telcordia Technologies, Inc.
- Current Assignee Address: US NJ Piscataway
- Agent Philip J. Feig
- Main IPC: G06F11/00
- IPC: G06F11/00

Abstract:
A computer implemented fault diagnosis method employing both probabilistic models and statistical learning that diagnoses faults using probabilities and time windows learned during the actual operation of a system being monitored. In a preferred embodiment, the method maintains for each possible root cause fault an a-priori probability that the fault will appear in a time window of specified length as well as maintaining—for each possible resulting symptom(s)—probabilities that the symptom(s) will appear in a time window containing the fault and probabilities that the alarm will not appear in a time window containing the fault. Consequently, the method according to the present invention may advantageously determine—at any time—the probability that a fault has occurred, and report faults which are sufficiently likely to have occurred. These probabilities are updated based upon past time windows in which we have determined fault(s) and their cause(s). Advantageously, each root cause fault may be assigned its own time window length. By maintaining these probability parameters for several different window lengths, a window length that is particularly well-suited to a particular set of conditions may be chosen.
Public/Granted literature
- US20110185229A1 FAULT DIAGNOSIS EMPLOYING PROBABILISTIC MODELS AND STATISTICAL LEARNING Public/Granted day:2011-07-28
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