Predictive machinery tests and monitoring have evolved significantly, offering a variety of techniques, methodologies, and tools. While many of these tests provide valuable diagnostics for specific machine types, only two technologies have consistently proven their value across a broad range of industrial applications: vibration analysis and oil analysis. These two methods deliver high benefit-to-cost ratios, making them essential components of a predictive maintenance program.
However, within these two disciplines, a wide range of testing methods and best practices exists, each with varying degrees of effectiveness. The key to maximizing the value of predictive maintenance lies in adopting these technologies and applying them effectively and consistently.
Azima DLI builds its approach on leveraging the most effective technologies and methodologies within vibration and oil analysis. This allows for more precise trend analysis, early detection of machine faults, and historical data comparison — all without significantly increasing the time or effort needed to gather this information.
Condition Assessment for Condition-Based Maintenance (CBM)
Condition-based maintenance (CBM) has been around long enough that most companies understand its philosophy and inherent value. But the problem facing plant management today is how to manage the enormous amount of data that comes from CBM via portable vibration data collectors and online systems that store machine performance information. A fundamental goal of plant management is to analyze the data in a way that will give a concise, accurate assessment of machine condition without costly, specialized labour.
The number of machines regularly tested in a monitoring program has grown so large in many plants that the personnel responsible for sifting through exception reports can easily become overwhelmed. This makes it difficult for technicians to dedicate the time for closer condition analysis.
When this happens, the success of the program is limited by the workforce 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.
The Azima solution moves up the ladder of technology to software-aided machinery health monitoring and harnesses the power of automated diagnostic systems to eliminate data overwhelm. With fault diagnostic software, the day-to-day management and analysis of voluminous vibration data reports shifts away from specialized staff at the facility and to an intelligent, automated system. Azima system diagnostics with the appropriate fault templates, machinery information, and baseline data, can process the machinery vibration data. The software can present detailed information on machine condition, faults, and degradation rates as part of a machinery fault report.
What Is an Automated Diagnostic System?
In the context of machinery diagnostic applications, the term automated diagnostic system generally refers to a computerized means of collecting and applying the knowledge from a pool of machinery vibration analysts, institutional knowledge, 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, etc.) and proceed toward a specific conclusion about the machine’s condition and its relative need for repairs. They move step by step between the computer and the analyst, following the observed machinery and vibration data down a branching network toward a diagnostic conclusion about the machine’s specific mechanical fault.
The Azima fault diagnosis system tests for all modelled faults and operates automatically without the need for human interaction. Once installed, the automated system can operate without human interaction on vibration and other machine information in the computer’s database in the majority of cases to arrive at specific conclusions about machinery condition and need for repairs. While setting up fault models requires an initial investment, the system can ultimately run with nearly zero labour expense.
How Does Azima Compare to Expert Analysis?
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, Azima 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 of our database of 11,800 tests shows a 94% agreement between Azima’s diagnostic system and experienced analysts.
Certification and debugging are simplified in the Azima diagnostic system because the software shares its diagnostic rationale together with the machinery faults. The diagnostic system can assess the relative importance of each machine fault and suggest priorities for repair planning, which is especially helpful on a limited budget. 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.
While automation streamlines routine diagnostics, Azima’s approach doesn’t replace human expertise—it complements it. When machine readings present complex or ambiguous results, Azima’s experienced vibration analysts step in to interpret the data and provide deeper insights. This balance allows teams to focus on critical decision-making while automating repetitive analysis tasks.
How the Azima Automated Fault Diagnostic System Works
The Azima software system has been rigorously field-tested for predictive maintenance of rotating machinery. It is comprised of four software modules:
- A 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. This is done by automatically converting 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 sophisticated enough to accurately synthesize normalized signatures from machinery undergoing speed changes during the vibration test and handle any motor-driven machine with ease.
- 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, which has captured over 95 years of knowledge and experience in machinery condition analysis. It contains over 4,700 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 experienced Azima vibration engineers to provide optimum diagnostic consistency and agreement with human analytical processes.
Conclusion
Azima’s intense emphasis on automation results in a system that provides a variety of standard outputs ranging from detailed machine repair recommendations to normalized vibration signature plots. The system is engineered so that all necessary mechanical details are pre-set in the system knowledge base and are automatically accessed when needed.
With advanced signal processing techniques like fast Fourier transform (FFT), order normalization, and cepstrum analysis, Azima’s intelligent system can search vibration signatures and detect subtle or hidden fault patterns and symptoms. It can aid repair planning and plant shutdowns by automatically setting priorities for repairs based on the relative severity of each fault.
By automating the condition monitoring process, Azima empowers users to make accurate, repeatable assessments of machine health, improving reliability and reducing costs.
Content Courtesy of Fluke Reliability. Optimal is a Fluke Reliability Platinum Partner.
Curious how this service applies to your business? Contact us at enquiries@optimal.world | www.optimal.world
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