Distributed stress wave analysis system

A method for identifying and analyzing a fault in one or more components of a machine, comprising the following steps:

• (a) detecting a fault in at least one component of a machine, said step of detecting a fault including the steps of identifying abnormal features in acquired data by a anomaly detection network followed by the performance of a first X of N confidence test, triggering a fault detection network to confirm or reject a possibly discrepant condition identified by said anomaly detection network and the first X of N confidence test, said fault detection network performing a second X of N confidence test for confirming or rejecting the discrepant condition identified by said anomaly detection network;
• (b) locating the detected fault;
• and
• (c) isolating a source for the fault.

A distributed stress wave analysis system for identifying and analyzing a fault in one or more components of a machine, comprising:

• at least one sensor;
• at least one distributed processing unit in communication with said at least sensor, said at least one distributed processing unit extracting shock and friction information received from said at least one sensor, wherein said at least one distributed processing unit performs extraction of feature information relating to time domain and frequency domain characteristics of the shock and friction raw signals received from said at least one sensor and compresses the raw data into extracted feature information;
• a maintenance advisory panel in communication with said at least one distributed processing unit, and
• a computer.

A method for identifying and analyzing a fault in one or more components of a machine, comprising the following steps:

• (a) extracting friction and shock signals from broadband noise received from at least one sensor associated with at least one monitored component of a machine;
• (b) detecting abnormal friction/shock signatures from the extracted signal, said step of detecting abnormal friction/shock signatures including the steps of identifying a possibly discrepant condition by an anomaly detection network and performance of a first X of N confidence test, which when passed triggers a fault detection network to confirm or reject the possibly discrepant condition identified by said anomaly detection network and the first X of N confidence test, and confirming or rejecting the discrepant condition identified by said fault detection network by performing a second X of N confident test;
• (c) locating a fault causing the detected abnormal friction/shock signature;
• (d) isolating a cause of the fault to a gear source or a bearing source.

A distributed stress wave analysis system for identifying and analyzing a fault in one or more components of a machine, comprising:

• means for detecting a fault in at least one component of a machine, said means for detecting including feature extraction software to intelligently compress a large amount of data acquired from machinery mounted instrumentation into a small amount of information that accurately characterizes the large amount of data, said extracted features serving as inputs to an anomaly detection network for identifying abnormal (anomalous) features in the data acquired from machinery mounted instrumentation, followed by the performance of a first X of N confidence test, said means for detecting also including a fault detection network triggered by said anomaly detection network and its associated X of N confidence test to confirm or reject a possible discrepant condition identified by said anomaly detection network, said fault detection network performing a second X of N confidence test for confirming or rejecting the discrepant condition identified by said anomaly detection network;
• means for locating the detected fault;
• and
• means for isolating a source for the fault.

A method for identifying and analyzing a fault in one or more components of a machine, comprising the following steps:

• (a) detecting a fault in at least one component of a machine, said step of detecting a fault including the steps of running feature extraction software to intelligently compress a large amount of data acquired from machinery mounted instrumentation into a small amount of information that accurately characterizes the large amount of data, said extracted features serving as inputs to an anomaly detection network to identify abnormal (anomalous) features in the data acquired from the machinery mounted instrumentation, followed by the performance of a first X of N confidence test, triggering a fault detection network to confirm or reject a possible discrepant condition identified by said anomaly detection network and its associated X of N confidence test, said fault detection network performing a second X of N confidence test for confirming or rejecting the discrepant condition identified by said anomaly detection network;
• (b) locating the detected fault;
• and
• (c) isolating a source for the fault.