Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Mechanical measurement system
Patent
1997-10-14
2000-02-15
Wachsman, Hal
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Mechanical measurement system
702 33, 702 54, 702126, 702183, 73659, G01H 1106
Patent
active
060263482
DESCRIPTION:
BRIEF SUMMARY
FIELD OF THE INVENTION
The present invention relates generally to a method and apparatus for compressing measurement data correlative to machine status, and in particular, to a method and apparatus for compressing machine vibration data to allow significant compression of the original data for storage and transmission wherein the compressed data can be retrieved and reconstructed to provide a complete continuous waveshape history of machine performance.
BACKGROUND OF THE INVENTION
Machinery monitoring systems have been permanently installed in, inter alia, today's large process plants, power generation stations and pipelines in an attempt to provide machinery protection by continuously monitoring the behavior and performance characteristics of machinery at a multiplicity of points and possibly acquiring data from these points simultaneously. More recently, the trend has been to enhance the monitoring systems by directly interfacing computers to the systems for periodically collecting data from these systems for historical trend, machinery diagnostics and predictive maintenance purposes. However, these current systems use methods which only retain a small history of machine performance at best.
For example, current systems periodically collect, store, display and print machinery data in a variety of formats using a variety of schemes. One such scheme is to continuously sample and store data at a high sample rate to obtain data with relatively high data time resolution, and as storage space fills, to replace the stored data with a new data set. This scheme does not automatically store the historical information necessary to analyze one or more problems, may not represent a long enough period of time to represent the on-set of one or more problems and does not readily identify the occurrence of one or more problems.
Another scheme is to intermittently capture data "snapshots" of the machine performance. A small set of "snapshots" are maintained in memory and saved in the event of a machine problem. However, the time represented by the "snapshots" may not be adequate to represent historical machine performance or may not represent a continuous set of data with the machine fault occurring between data sets previously stored in memory.
A common scheme is to represent the machine performance with an overall magnitude, eliminating all of the details that are contained to generate the magnitude. Although the magnitude can be used for protection, it does little to identify the causes of the problem.
The disadvantage of these schemes is they either consume too much memory, may not provide a rapid method to identify when one or more problems commence and to describe its progress or may lose the ability to diagnose one or more problems after the fact by either destroying the data with replacement information, or by taking data samples with the data of interest falling between the samples.
Therefore, if one were to continuously capture the machine data using current techniques, the memory requirements of such data storage can be enormous considering that the data is preferably collected over a period of months or years. In addition, long transmission times are required for transmitting large quantities of continuous machine data to a remote data base for permanent storage and with enough detail and history to perform fault analysis and diagnosis.
In addition, with current systems it is a challenge to capture and store infrequently occurring machine anomalies and to ensure that these anomalous events get managed using past learning experiences and procedures according to historical data. For example, the cause of and the procedures needed to deal with these machine anomalies may not be repetitive enough to stay within peoples' memory. Further, to make matters worse, many anomalous events occur so infrequently that people who managed and learned from previous situations have either changed jobs or are not available by the time a similar anomalous event occurs again. These anomalous events can have a profound impact if
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Bently Nevada Corporation
DeBoo Dennis
Wachsman Hal
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