The field of predictive machinery test and monitoring has developed a wide range of techniques, methodologies and instrumentation. Although many of the tests are valuable diagnostic tools and are specialized to work well for very specific machines, there are only two technologies that have application to a general population of machinery and have proven to have consistently high benefit to cost ratios. These two technologies are vibration and oil analysis for predictive maintenance. Within the field of these two technologies however, there are a wide range of tests and practices with varying effectiveness.
Symphony Industrial AI applies the best available technology and practice within these two fields. Our advantage lies in providing a sophisticated system on a ongoing, cost-effective basis. An example of this is from the field of vibration analysis: the common approach is to use less detailed, broadband measurements as a screening tool and then to go back in individual cases with the more sophisticated, narrowband equipment. This method has severe drawbacks. The broadband screening usually fails to detect the early warning low level components of the vibration spectra. When narrowband analysis is required, there is no corresponding historical narrowband data for comparison or trending. In contrast, our approach is to make a full set of dual frequency range measurements at every monitoring point. This is practical, allows for more accurate calls through trending the machine history and requires only an insignificant increase in time/effort.
Symphony Industrial AI has been able to utilize this practical and sophisticated application of technology because of dedicated personnel who have climbed the steep knowledge/learning curve, invested in specialized equipment and who apply research & experience which is reflective in the product and service itself.
The result is a straightforward report of specific faults, corrective actions and priorities. Our product for the customer is information, not just data.
Condition Assessment for Condition-based Maintenance (CBM)
The problem facing plant management today is not so much one of accepting condition based maintenance as a philosophy, but rather how to manage the enormous amount of data that the new generation of portable vibration data collectors and/or permanent online systems can bring back to the maintenance department’s computer. A fundamental goal of plant management is the analysis of the data in a manner that will give concise, accurate assessment of machine condition, and do so without costly, specialized labor.
The number of machines that can be regularly tested in a monitoring program has grown so large in many plants that the number of machines picked up in the exception reports may overwhelm the human resources available for closer condition analysis. When this happens, the success of the program is limited by the manpower available to manage the data, and not by the instrumentation used to collect the data. The most common solution is to sacrifice a great deal of capability and utilize a simplistic broadband screening/alarm approach.
Symphony Industrial AI solution is to move up the ladder of technology to computer-aided machinery health monitoring, and to employ the power of automated diagnostic systems. With fault diagnostic software, the day-to-day management and analysis of the voluminous vibration data reports is shifted away from the specialized staff and onto the computer. The computer with the appropriate diagnostic fault templates, machinery information, and baseline data can process the machinery vibration data and present detailed information on machine condition, faults, and rates of degradation as part of the Machinery Fault Report.
Just What is an Automated Diagnostic System?
In the context of machinery diagnostic applications, the term generally refers to a computerized means of collecting and applying the knowledge from a pool of machinery vibration analysts, corporate maintenance “folklore” and other valuable expertise. Most intelligent diagnostic systems for machine condition diagnostics are ‘forward chaining”. That is, they begin with a set of facts (vibration amplitudes, inspection notes, operating conditions,...) and proceed toward a specific conclusion about the machine’s condition and its relative need for repairs. They proceed step by step interactively between the computer and the analyst from the observed machinery vibration data and symptoms down a branching network toward a diagnostic conclusion about the machine’s specific mechanical fault.
Symphony Industrial AI fault diagnostic system tests for all modeled faults, and operates automatically without the need for human interaction. Once installed, the automated fault diagnosis system can operate without human interaction on vibration and other machine information in the computer’s database to arrive at specific conclusions about machinery condition and need for repairs. Also, because it can operate with human interaction, this type of system can operate on-line to continuously monitor and trend machinery health. While our system generally has a somewhat higher startup investment for installation of the fault models, it can ultimately be run in a “lights out” mode in the PC with nearly zero labor expense.
The litmus test for judging the success of the software is to compare its machinery condition diagnoses to those made by skilled vibration analysts. In one such test, Symphony Industrial AI Engineering furnished its fault diagnostic system with the rules to diagnose any of thirty possible faults in a petroleum product purifier. For this test, the system examined vibration signatures from 113 machines. In all diagnoses, the system matched or exceeded the performance of the human analysts. Further analysis on our database of 11,800 tests has shown a 94% agreement between Symphony Industrial AI’s diagnostic system and the experienced analysts.
Certification and debugging of Symphony Industrial AI’s Automated Diagnostic System are simplified because the software prints its diagnostic rationale together with the machinery faults. To help the repair planner allocate limited repair dollars, the diagnostic system can assess the relative importance of each machine fault and suggest priorities for repair planning.
Consistent with the goal of reducing the labor required to manage vibration data from the predictive maintenance program, the specific fault trend plots help the maintenance supervisor make quick decisions about machine condition and repair or shutdown plans.
The Symphony Industrial AI fault diagnostic system has been used successfully with vibration data from a wide variety of modern data collectors. To realize the full potential of the system, the measurements should include high-resolution narrow band FFT spectral measurements, orthogonal measurements to describe motion in all three axes, and demodulation of high frequency vibration data. The Symphony Industrial AI Watchman vibration instruments are routinely programmed to efficiently take all the required measurements. Several vibration data collectors and analyzers from other sources can be used to make the required measurements.
Symphony Industrial AI Automated Fault Diagnostic System
This is a software system that has been rigorously field tested for Predictive Maintenance of rotating machinery. The system runs in the PC environment and is comprised of four software modules:
• Symphony Industrial AI WATCHMAN Vibration Monitoring module for test point setup, route management, conventional vibration analysis, and communication with the portable data collection unit.
• An Order Normalization module that examines the fixed frequency vibration signatures gathered by the data collector and accurately determines the running speed of each machine during its vibration test.
• A Spectral Screening module that automatically extracts significant features and vibration signatures that are necessary for assessment of machine condition by the Expert Rule module.
• An Expert Rule module that has captured over 95 man-years of knowledge and experience in machinery condition analysis.
The Order Normalization module is an internal software module that automatically converts data collected in fixed frequency and without a 1/revolution tachometer into signatures based on an abscissa of shaft rate multiples (orders). The unique Order Normalization module has been refined and tested over a period of several years and is so sophisticated that it is capable of accurately synthesizing normalized signatures from machinery undergoing speed changes during the vibration test and can handle any motor driven machine with ease.
The Spectral Screening module provides the diagnostic system with its primary spectral data. It has been meticulously designed and developed by Symphony Industrial AI’s engineers to conduct a thorough examination of the vibration signature data collected from a machine and to distill that data into a matrix summary of numerical and logical constants that completely describe important features of the vibration signature for a machine. The screening module even uses a unique application of Cepstrum analysis to identify low amplitude bearing tones and gear mesh problems. The Cepstrum analysis is also used to detect faulty vibration data such as may be caused by abnormal operating conditions or operator error during the data collection process.
The Expert Rule module contains over 4700 individual rules and can recognize 956 specific machine fault patterns in 47 types of machines or machinery components. The rule base is continually expanded and fine-tuned by Symphony Industrial AI’s experienced vibration engineers to provide optimum diagnostic consistency and agreement with the human analytical process. The Symphony Industrial AI diagnostic system is a frame-based backward-chaining system that was written and developed by Symphony Industrial AI engineers specifically for assessing machine condition. It is not a generic diagnostic system shell that has been converted for machinery analysis.
Once it has been set up to recognize vibration signatures from the plant’s machinery, the diagnostic system automatically handles the entire condition analysis process. In the design of the Automated Fault Diagnostic System, Symphony Industrial AI has taken a component-based approach to automated analysis. That way, the system treats the machine as the sum of its component parts. Thorough analysis is accomplished by considering each component (motor, gearbox, pump, fan, coupling, etc.) as a partially isolated machine and applying groups of rules that are keyed to each component. This component-oriented scheme makes the automated diagnostic system an exceptionally powerful and flexible tool that can easily handle a wide variety of machine types.
Symphony Industrial AI’s intense emphasis on automation has resulted in a system that can be set to provide a variety of standard outputs ranging from detailed machine repair recommendations to normalized vibration signature plots -- all with no manual user input and at the very lowest cost per machine tested. The system is engineered so that all of the mechanical details to be analyzed from the machines are preset in the system knowledge base and are automatically accessed whenever necessary. No question/answer sessions are necessary to get automated analysis of all your machines.
Intelligent systems empower their users to make accurate, repeatable condition assessments, fault diagnoses and repair recommendations about machinery, and to solve problems that would have previously required expensive skills of several key persons in the plant’s maintenance or engineering organizations.
Today, intelligent system software is available that can “screen” machinery vibration signature data and duplicate the expertise of the skilled vibration analyst. With advanced signal processing techniques like FFT, order normalization, and Cepstrum analysis, an intelligent system can search vibration signatures and detect subtle or hidden fault patterns and symptoms that might be missed by even the most skilled human analyst. Because automated diagnostic systems do not tire, change moods or have a limited attention span, they will ensure consistency in machinery fault diagnoses and repair recommendations.
Intelligent system software can aid planning for repairs and plant shutdowns by automatically setting priorities for repairs based on the relative severity of each of a large number of machinery faults. In this way, a properly installed system will help ensure that the plant’s limited repair resources are spent in the way most beneficial to plant operations and profitability.