A method for operating digital electronic appliance that empanels several different artificial intelligence (AI) classification technologies into a “jury” uses combinational digital logic to render “verdicts” about the need for service and impending equipment failures of the machines they monitor. Networks can be used to forward signals from remote locations to a centralized appliance that may be plugged as a module into a server. The appliance outputs can also be communicated over networks to servers that will muster appropriate maintenance personnel who are forewarned as to the nature of the trouble
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for monitoring the operation of machines and for issuing calls for preventative maintenance and predictions of equipment failures, comprising: attaching monitoring devices, instruments, and transducers to a machine subject to operational failures; reading in measurements and data obtained by the monitoring devices, instruments, and transducers regarding the status and operation of the machine; empanelling a jury of classification models as jurors to assess the measurements and data obtained with a separate computer programmed for that purpose; presenting all the measurements and data obtained to the jury with a separate computer programmed for that purpose; classifying the measurements and data obtained and presented to the jury according to a logic decision tree and outputting a juror vote that includes a confidence assessment with a separate computer programmed for that purpose; classifying the measurements and data obtained and presented to the jury according to a neural network and outputting another juror vote that includes a confidence assessment with a separate computer programmed for that purpose; classifying the measurements and data obtained and presented to the jury according to a fuzzy logic and outputting another juror vote that includes a confidence assessment with a separate computer programmed for that purpose; classifying the measurements and data obtained and presented to the jury according to a smart agent profiling and outputting another juror vote that includes a confidence assessment with a separate computer programmed for that purpose; classifying the measurements and data obtained and presented to the jury according to business rules and outputting another juror vote that includes a confidence assessment with a separate computer programmed for that purpose; classifying the measurements and data obtained and presented to the jury according to case-based reasoning and outputting another juror vote that includes a confidence assessment with a separate computer programmed for that purpose; collecting all the juror votes into a single ballot and mathematically apply individual weights in calculations to each respective juror vote with respect to its own confidence assessment and a priori data inputs with a separate computer programmed for that purpose; tallying a verdict from the results obtained in the previous steps, and that predicts an operational failure of the machine by outputting a report with a separate computer programmed for that purpose; and tallying another verdict from the results obtained in the previous steps, and that summons a particular service procedure and/or a replacement part for the machine by outputting another report so the costs of maintaining the machine are reduced. 29. The method of claim 1, further comprising: classifying the measurements and data obtained and presented to the jury according to an associative-learning juror included within the jury, and that calculates its decisions on the basis of associations between responses observed at its inputs to any particular stimulus it has previously learned, and outputting another juror vote that includes a confidence assessment. 3. The method of claim 1, further comprising: classifying the measurements and data obtained and presented to the jury according to clustering logic included within the jury panel, and that calculates its output decisions on the basis of clusters observed in the input data, and outputting another juror vote that includes a confidence assessment. 4. The method of claim 1, further comprising: classifying the measurements and data obtained and presented to the jury according to an optimization logic included within the jury panel, and that calculates its output decisions on the basis which combinations of values then at its inputs are constrained as allowable and which combinations are not, and outputting another juror vote that includes a confidence assessment. 5. The method of claim 1, further comprising: classifying with a separate computer programmed for that purpose the measurements and data obtained and presented to the jury according to a text mining logic included within the jury panel, and that bases its output decisions on an automated process of extracting information from analyses of unstructured texts at its inputs, and outputting another juror vote that includes a confidence assessment. 6. The method of claim 1, further comprising: data cleaning between a machine monitoring device and the jury panel, and including data-correction, data-deletion, and data-formatting to produce a single-format data structure at its output. 7. The method of claim 1, further comprising: data enriching between a machine monitoring device and the jury panel, and including data-insertion, data-computation, and data-formatting logic in combination to produce an enriched single-format data structure at its output. 8. A group of electronic appliances for monitoring the operation of machines and for issuing calls for preventative maintenance and predictions of equipment failures, comprising: a monitoring device attached by instruments to a machine and having electrical inputs and transducers that measure in parallel a variety of operational parameters of the machine and that are then electrically represented by voltages, currents, and/or digital data; a jury panel comprising a digital logic assembly of several juror modules with electrical input connections that each receive the same operational parameter measurements of the machine at the same time, and that each produce an independent juror vote decided in a different way and communicated as a juror-vote digital output; a smart-agent juror module included with the jury panel and that includes a real-time profiling logic and storage memory, a long-term profiling logic and storage memory, and a recursive profiling logic and storage memory, in parallel between its inputs and outputs; a data-mining juror module included with the jury panel and that includes a logic decision tree between its inputs and its outputs; a neural-network juror module included with the jury panel and that includes neural network logic between its inputs and its outputs; a fuzzy logic juror module included with the jury panel and that includes fuzzy logic between its inputs and its outputs to automatically cluster information into a plurality of risk categories and decreases sensitivity to noisy or outlier data; a business-rules juror module included with the jury panel and that includes business rules logic between its inputs and its outputs; a case-based reasoning juror module included with the jury panel and that includes representations of previous case sets of operational parameters of similar machines and past decisions determined to be successful ones, and a comparator to match these previous cases to a current set of operational parameters of the machine provided at its inputs, and an output for its conclusions; an associative-learning juror module included with the jury panel, and that calculates its output decisions on the basis of associations between responses observed at its inputs to any particular stimulus it has previously learned; a clustering juror module included with the jury panel and that includes clustering logic between its inputs and its outputs; a metarule arbiter module comprising several weight calculators each respectively attached to receive a corresponding one of said independent juror-vote digital outputs, and a digital summation device that produces a single composite prediction output from a set of several weighted sums of said dependent juror vote digital outputs; and an output device that transforms the composite prediction output into human-readable form. 9. The group of electronic appliances of claim 8, further comprises: an optimization-suite juror module included with the jury panel, and that includes optimization logic that specifies which combinations of values then at its inputs are constrained as allowable and which combinations are not, and that produces its decision at its outputs as digital data. 10. The group of electronic appliances of claim 8, further comprises: a text mining juror module included with the jury panel and that includes text mining logic between its inputs and its outputs as digital data. 11. The group of electronic appliances of claim 8, further comprises: a data cleaning device connected between the machine monitoring device and the jury panel, and that includes data-correction, data-deletion, and data-formatting logic that combine to produce a single-format data structure at its output. 12. The group of electronic appliances of claim 8, further comprises: a data enrichment device connected between the machine monitoring device and the jury panel, and that includes data-insertion, data-computation, and data-formatting logic in combination to produce an enriched single-format data structure at its output. 13. The group of electronic appliances of claim 8, further comprises: a velocity-analyzer juror module included with the jury panel and that includes velocity analyzing and data compression logic between its inputs and its outputs. 14. The group of electronic appliances of claim 8, further comprises: an optimization suite juror module included with the jury panel and that includes optimization logic that specifies which combinations of values then at its inputs are constrained as allowable and which combinations are not, and that produces its decision at its outputs.