Predictive maintenance relies on pinpointing the right insights at the right time. With the growth of cloud computing and emerging sensor technologies, the sheer volume of machine data that most teams must process makes reliable analysis more cumbersome than ever. Analysts face mounting pressure to interpret vast amounts of information quickly and accurately, a task that feels increasingly unsustainable without a fundamental shift in approach.
The solution isn’t just automation — it’s smarter automation. By cutting through the noise and focusing on critical issues, automation enables analysts to concentrate on solving problems instead of sorting through endless reports. It’s an approach that keeps machines running efficiently and transforms how teams think about maintenance altogether.
This is how smarter automation is reshaping vibration analysis — and what it takes to get it right.
The Breaking Point for Manual Analysis
For decades, vibration analysis has relied on human expertise. Analysts would pore over data, graph by graph, hunting for patterns that signaled a problem. But a single machine test can generate 108,000 lines of data, which is a staggering amount to process and interpret manually. Analysts had to rely on their pattern recognition skills and experience to make sense of this information, identify anomalies, and decide which issues required action.
Historically, most plants used walk-around devices, where analysts would physically test machines and later analyze the data at their desks. These tests were typically performed monthly, though some plants stretched the interval to once a quarter due to resource constraints.
So, with each analyst managing between 100 and 500 assets per month, the workload was substantial even before the data explosion brought on by new technology. The rise of wireless sensors and online monitoring systems tipped the balance. With these systems delivering 30 times more data than traditional walk-around devices, an avalanche of information was making routine maintenance complicated and cumbersome. Remember, more data doesn’t necessarily mean better insights.
At one refinery, for instance, a fleet of 1,600 machines with 3,500 newly added wireless sensors left their six analysts grappling with the unexpected. At first, it felt like a relief: no longer was it necessary to walk around in the Texas heat collecting data. But when the sensors went live, they created instant data overload. Suddenly, the team had 3,600 sensors to monitor every day and had to evaluate which was or was not a problem machine. Instead of gaining more insights, they got less.
Faced with this new reality, the question wasn’t if automation was needed — it was how to make it truly effective.
The Challenges of Automation
Now, automation sounds simple in theory: let the machines handle the data, and analysts can step in when it matters most. But making it work in practice is anything but straightforward.
One of the biggest challenges is adapting automation to handle the sheer complexity of vibration analysis data. While a human analyst might rely on intuition and experience to make sense of patterns, automation requires rules and algorithms precise enough to process data at scale. A single machine test might reveal critical forcing frequencies, but teaching a system to identify and prioritize these in a meaningful way is no small feat.
Another hurdle is accounting for variability. Machines don’t always operate at fixed speeds or under consistent conditions, especially with the rise of variable frequency drives (VFDs). Automation, therefore, must account for these differences seamlessly, delivering reliable results regardless of fluctuating speeds or behaviors.
Finally, there is the issue of trust. Analysts, long accustomed to manual control, are understandably skeptical. Can automation handle the complexities they encounter daily? Will critical faults slip through the cracks? Winning their confidence requires automation systems to consistently demonstrate accuracy and reliability — earning their place as a tool for collaboration rather than replacement.
These challenges highlight the need for smarter, more adaptable systems that can work alongside analysts rather than attempting to supersede their expertise.
Azima’s Approach to Automation
To address these challenges, Azima combines innovative algorithms, thoughtful design, and a focus on scalability.
Smarter Data Processing
Azima’s solutions are designed to handle the overwhelming volume of machine data analysts face daily. Azima’s automation tools filter through each individual line of data, isolating actionable patterns and anomalies while removing routine noise.
One key advancement that enables Azima’s capabilities is the use of synthetic baselines built on ISO standards, which serve as a foundational reference for each machine’s expected performance. These synthetic baselines provide an initial reference point for vibration behavior, which is then continuously refined as Azima’s solution learns more about the particular machine’s vibration characteristics.
To achieve this, Azima employs a structured process of analysis and verification. Over the first 4-8 months for a walk-around program — or within weeks for a wireless setup — the system refines its statistical averages to match machine-specific vibration signatures that represent what is considered to be a normal, or baseline level of vibration. The system then utilizes these vibration baselines as a direct source of comparison alongside incoming test data. The system applies rules-based logic to prioritize actionable insights, delivering results such as identified faults and recommended actions directly to analysts.
By the time analysts begin their graphical reviews, they already have the fault identified, saving significant time and enabling faster, more informed decision-making. This structured process has been a cornerstone of Azima’s expert system since 1990, providing a significant speed advantage over manual approaches.
Adapting to Real-World Variability
As any vibration analyst knows, great analysis begins with proper normalization. Machine behavior often changes between tests, especially in modern systems with variable frequency drives. However, Azima’s speed normalization algorithms solve this by adapting to changes in speed and conditions. Whether a machine’s operating speed and/or laod shift slightly or significantly, the system does a remarkable job of automatically normalizing the data to maintain consistent analysis.
In addition to providing robust vibration baselines, the Azima AI system is also capable of automatically classifying and extracting prominent forcing frequencies from vibration data, which is essential to providing quality analysis. Forcing frequencies such as pump vanes, gear teeth, and motor bars are discerned using labeled historical vibration signatures alongside advanced machine learning algorithms to extract the information from the vibration data. This feature, which analysts previously managed manually, has streamlined data analysis and reduced the time it takes to complete intensive tasks.
Streamlining Workflows with Persistent Fault Logic
When serious or extreme faults are identified by a wireless or online system and have been reviewed by the team of experienced Azima analysts, the Azima solution adapts itself to reduce the need for further analysis if the diagnostic results continue to persist in the days afterword. If the machine condition worsens or a new fault begins to emerge, then the Azima analyst will be called to investigate again. This moves the system closer to achieving higher levels of automation.
For example, if a fault like extreme motor bearing wear is flagged, an analyst reviews and confirms the severity. If subsequent tests show the fault remains stable and within predefined guardrails, the system automatically sends updated results directly to the customer. Analysts only need to revisit the fault if its condition changes, such as the addition of coupling wear or similar. This automated analysis workflow, significantly reduces repetitive reviews without decreasing the quality of analysis.
Delivering Scalability
This monitoring capability also enables 24-hour turnaround times for wireless data results on critical issues — a significant improvement over traditional approaches, which often take up to five days.
Azima’s workflow management tool further streamlines processes by assigning tasks daily, ensuring that analysts focus on high-priority items without wasting time searching for faults that need attention.
How do Azima analysts manage to deliver scalable on-time analysis across a wide range of customers?
They rely on a workflow management system to streamlines processes by assigning and deploying analysts to machines that need reviewed. When paired with the automated diagnostic system performing initial analysis, Azima ensures that analysts focus on high-priority items without wasting time searching for faults that need attention. The effect is quite similar to the triage process implemented in the medical world.
Remember the refinery that added 3,500 sensors to its existing fleet of 1,600 machines? The sudden influx of data overwhelmed the six analysts tasked with monitoring it, making manual analysis unsustainable. Azima addressed this for the customer by running their data through the Azima solution, which includes the automated diagnostic system and its thousands of rules as well as the highly efficient workflow management system, ultimately returning the results of the diagnostics back to the customer’s analysts easily and efficiently.
Serious and extreme faults were flagged for immediate review by the analyst, while routine issues, such as slight misalignments, were automatically sent to the customer with actionable recommendations. The system’s persistent fault logic monitored ongoing issues, ensuring that analysts only revisited faults when conditions changed, reducing unnecessary workload.
By automating these processes, Azima helped the refinery manage its massive data volumes effectively, allowing analysts to focus on critical tasks without sacrificing accuracy or speed.
Empowering Analysts
Expert analysts remain central to Azima’s approach. While routine issues like alignment faults can be handled automatically, more nuanced problems requiring deeper expertise are flagged for review.
Automation also works to improve itself. Features like confidence scoring show how reliable a system’s diagnosis is based on available data. For instance, if a system flags a serious fault like motor shaft looseness, the confidence score clearly indicates whether the result is supported by sufficient information. If too much data is missing, the system issues a warning, prompting further review.
Alongside this, completion scoring highlights any missing details about a machine’s profile, such as the number of motor bars. These gaps are tracked and addressed over time to ensure ongoing improvements to the system’s accuracy and reliability. This transparency helps teams trust Azima.
Looking Ahead
The journey toward smarter automation has only just begun. While automation has transformed vibration analysis, the role of analysts remains vital — especially as customer needs continue to evolve. Some customers embrace automation fully, relying on systems to handle everything from routine faults to complex diagnostics. Others prefer manual review for critical assets, such as million-dollar compressors, where human oversight provides an extra layer of assurance.
Azima’s tailored approach shows that automation is not a one-size-fits-all solution; it’s flexible, adapting to individual customer needs. This dual approach — combining scalable automation with expert input — will define the next phase of predictive maintenance.
By focusing on collaboration and customization, smarter automation ensures that as the volume of data grows, the insights remain clear and actionable. As technology advances, automation systems will become even more adaptable, blending seamlessly with customer workflows while preserving the trust and precision that only analysts can provide.
This blog post is based on an Xcelerate 24 session titled Automation: Vibration by the Numbers, presented by Steven Hudson, Director of Operations at Fluke Reliability.