Many industrial manufacturers have valuable sensor data but no record of past equipment failures, making traditional predictive maintenance approaches difficult.
You operate critical machinery, and unplanned downtime is costly. You've invested in instrumentation – PLCs, SCADA, historians – collecting a wealth of sensor data. The goal is clear: move beyond reactive or calendar-based maintenance to predict issues before they stop production. However, a common hurdle quickly emerges: most predictive maintenance vendors and off-the-shelf AI solutions ask for historical failure data.
The problem? Your machines rarely fail catastrophically, or when they do, the data leading up to it isn't neatly labeled as 'failure mode X occurred on date Y.' Perhaps maintenance logs are inconsistent, or the expertise to connect sensor readings to specific failures resides with retiring staff. This lack of labeled failure examples, while a testament to your operational reliability, becomes a significant barrier to adopting many data-driven maintenance strategies.
Most machine learning models, particularly those used for classification or regression, rely on 'supervised learning.' This means they need to be trained on examples of both normal operation and known failures. To predict a specific type of pump bearing failure, for instance, the model needs to see many instances of healthy pump operation and many instances of that specific bearing failure, along with the sensor data that corresponds to each state.
Without these explicit labels, supervised models cannot learn the patterns that precede a failure. They cannot connect sensor anomalies to specific outcomes if they haven't been shown what those outcomes look like in the data. This fundamental requirement is why many conversations with potential vendors quickly reach an impasse when you explain your lack of failure history.
Instead of trying to teach a system what a failure looks like, an alternative and often more practical approach is to teach it what *normal* looks like. This is the core principle behind unsupervised anomaly detection. Rather than needing labeled examples of every possible fault, these systems learn the intricate, multi-dimensional patterns of healthy machine operation from your existing sensor data.
When a machine begins to deviate from its established healthy signature – even subtly – the system flags it as an anomaly. This deviation might be the earliest physically observable onset of degradation, long before a threshold is tripped or a human observer notices a change. The advantage is clear: you don't need a history of failures; you only need data representing healthy operation, which most instrumented facilities already possess.
Defining 'normal' for industrial machinery is more complex than simply taking an average. Machine behavior is dynamic. A pump's vibration signature will naturally change with flow rate, a motor's current draw will vary with load, and a dryer's temperature profile will shift based on the material being processed. These are not anomalies; they are expected variations within healthy operation.
An effective system for learning normal behavior must be 'context-aware.' It needs to understand the relationships between different sensor signals and how they change under varying operating conditions. For example, it should recognize that a higher temperature is normal when a machine is running at full capacity, but abnormal if it occurs during idle periods. The goal is to detect *unexplained* deviations from these context-dependent healthy patterns, providing a true early warning of degradation.
When assessing vendors who claim to operate without failure histories, ask specific questions to ensure their approach aligns with your needs and delivers genuine value:
Look for systems that explain *why* an anomaly was flagged. A simple 'anomaly score' is not enough. You need to know which specific sensor signals changed, in what direction, and how that relates to the machine's normal behavior. This helps your team plan maintenance actions.
Consider the deployment model. Does the system run on-site, processing data at the edge or within your existing network, or does it require sending all your operational data to the cloud? For many manufacturers, keeping data on-site is a priority.
Inquire about ongoing maintenance. Does the system require continuous tuning, threshold adjustments, or a dedicated data science team to operate? The ideal solution should be operable by your existing maintenance and reliability staff.
Ask for proven results that demonstrate detection without labels. For example, Agent Athena™ has shown its capability in real-world scenarios. It caught a failure ~4 days ahead on a production ion-beam tool (real production data, blind) with an AUROC of 0.851 and ~12% FPR. On a high-volume cleaning operation (real production data), it flagged 1,500+ high-risk parts with an AUROC of 0.885. The system also detected a regime change days early in a water network (live field data) using unsupervised detection. For method verification, all modeled fault types were detected in a deposition chamber (synthetic simulation).
Understand the integration requirements. Can the system connect directly to your existing PLCs, SCADA, or historians with minimal disruption? A solution that works with your current instrumentation provides far sooner results.
Finally, be clear about the lead time for detection. While no system can give a blanket number, vendors should be able to discuss typical lead times in terms of 'days to weeks depending on the failure mode,' based on their method's ability to spot early degradation.
Agent Athena™ is designed specifically for industrial manufacturers who have sensor data but lack labeled failure histories. It learns each machine's healthy signature directly from the sensors already on it, without requiring any labeled failure examples or manual threshold tuning. This approach allows it to detect degradation at its earliest physically observable onset.
The system explains which signal changed and what to do, providing actionable insights for your maintenance team. It runs on-site, integrating with your existing operational technology, and does not require an internal AI or data science organization to operate. Multiple U.S. patent applications filed in 2026 and now pending protect the underlying technology.
What if I have some failure data, but it's incomplete or inconsistent?
Even if you have some historical failure data, systems that learn normal behavior can still be highly effective. They provide a robust baseline for anomaly detection, and any existing failure data can sometimes be used to validate or refine the understanding of specific degradation patterns, though it's not a requirement for initial deployment.
How quickly can a system like this be deployed and start providing value?
Deployment timelines depend on the complexity of your existing instrumentation and IT infrastructure. However, because these systems do not require extensive data labeling or model training on failure events, they can typically begin learning and providing insights much faster than traditional supervised learning approaches. Initial insights can often be seen in weeks.
What kind of sensor data is most useful for learning normal behavior?
Systems that learn normal behavior can use a wide range of sensor data, including vibration, temperature, pressure, current, voltage, flow rates, and operational states. The key is having continuous, time-series data that reflects the machine's operational conditions. The more diverse and granular the sensor data, the more comprehensive the understanding of 'normal' behavior can be.
Explore how Agent Athena™ can help your operations detect degradation early, without needing historical failure data, on our product page. Read more →