AI and Failure Analysis – Is It Possible to Detect the Next Failure Before It Happens?

Modern engineering systems generate vast amounts of data: temperature, vibration, loads, pressures, currents, operating hours, alerts, maintenance events, test results, and failure reports. When this information is collected over time, it may contain signs of a change in system behavior. The challenge is to identify them in time. This is where AI and advanced data analysis tools can help. ## From Post-Failure to Early Detection In the traditional approach, failure investigation often begins after the event. A component fails, a system is shut down, or a complaint is received – and then the investigation process begins. Data is collected, operating conditions are examined, and efforts are made to understand the failure mechanism and the root cause. When suitable historical and operational data exists, one can ask an additional question: Was there a pattern that could have been identified before the failure occurred? For example, a gradual change in vibration, a rise in temperature, a shift in current consumption, or an unusual combination of several parameters. ## What Can AI Look for in the Data? A complex system can have hundreds or thousands of data points over time. Advanced analysis tools can assist in identifying: * Behavioral anomalies * Gradual trends * Unusual combinations of variables * Patterns that recur before failure events * Clusters of events with similar characteristics This capability is particularly interesting when it is difficult to pre-define a simple threshold that separates "normal" from "abnormal." Instead of looking at a single measurement point, one can examine system behavior over time. ## Anomaly Detection – Identifying That Something Has Changed One of the possible uses is Anomaly Detection. The goal is not necessarily to immediately determine what is about to fail. The system can detect that a certain behavior is not similar to the normal behavior observed in the past. This is an important distinction. An anomaly is a signal for investigation, not necessarily proof of failure. A change in behavior may stem from an evolving fault, but also from a change in operating conditions, the product, the environment, a sensor, or a process. Therefore, the alert must be evaluated within the engineering context. ## Can AI Really Predict Failure? Sometimes yes – but not simply because AI is being used. Predictive capability depends heavily on data. Relevant and high-quality data are required, and sometimes a sufficient history of both normal states and failures is needed. If a specific failure event has rarely occurred in the past, there may not be enough information to build a reliable predictive model. Also, a system that changes over its lifetime can pose a challenge: a change in design, usage conditions, suppliers, or software can alter data patterns. Therefore, the right question is not: "Do we have AI?" But rather: "Do we have data that allows us to learn something useful about the system's behavior?" ## Predictive Maintenance – Maintenance Based on Condition One of the well-known applications of data analysis is Predictive Maintenance. Instead of performing maintenance only according to a fixed schedule or waiting for a failure, condition data is used to support the decision of when intervention is required. For example, one can monitor equipment behavior and identify deterioration that may justify inspection or treatment. The goal is not to "guess the future." The goal is to use information already existing in the system to make better maintenance decisions. ## AI Does Not Replace Failure Analysis When a model identifies an unusual pattern, the question remains: Why is this happening? This is where reliability engineering is required. One needs to understand the system, the potential failure mechanisms, environmental conditions, loads, interfaces, and operational history. AI might point to a correlation between variables. It does not necessarily prove causality. Therefore, it is appropriate to view it as an additional tool in the reliability engineer's toolkit, alongside Failure Analysis, testing, engineering models, and statistical analysis. ## The Connection to FMEA and FMECA FMEA and FMECA deal with the systematic identification of potential failure modes and understanding their impact and risk. Data from the field can enrich the process. If data analysis points to a pattern that recurs before a certain type of fault, the information can serve as input for engineering discussion: Is a specific Failure Mode occurring at a frequency different from what we estimated? Are there operating conditions that increase risk? Is additional control needed? Can an early warning sign for failure be identified? Conversely, FMEA can help focus data analysis on failure mechanisms and parameters with engineering significance. This creates a synergy between engineering knowledge and Data Analytics. ## The Connection to FRACAS The FRACAS – Failure Reporting, Analysis and Corrective Action System centralizes information on failures, investigations, and corrective actions. When data is consistently documented, it can become an important basis for analysis. AI tools can help, for example, in locating similar events, classifying reports, and identifying phenomena that recur across multiple systems or over time. Instead of each event remaining a separate record, one can attempt to transform accumulated information into useful engineering knowledge. ## And What About Textual Data? Not all failure information comes from sensors. A large part of it is found in text: failure reports, complaints, service reports, investigation results, technician descriptions, Lessons Learned. AI tools for language processing can assist in organizing large amounts of such information, searching for similar events, and surfacing recurring topics – as we detailed in our guide on AI in quality management. Here too, professional verification is required. Similarity between two descriptions does not prove that both failures were caused by the same mechanism. ## Data Quality Is Part of Reliability Engineering An AI system cannot magically compensate for unreliable data. If sensors are not calibrated, failure reports are inconsistent, or important data is missing – the analysis may also be problematic. Before building an advanced predictive capability, it is worth asking: Are we collecting the right data? Are the failure definitions uniform? Do we know under what conditions the system operated? Is the data traceable throughout the lifecycle? Sometimes the first step toward AI is not a new algorithm. It is improving the data infrastructure. ## And What About False Alarms? A system that alerts on every small change can quickly become a system that is ignored. Therefore, there is also significance to False Positives – situations where an alert is received but there is no actual failure. On the other hand, a model that is not sensitive enough might miss significant events. The balance between the two should be determined according to the engineering and business implications. In a critical system, the meaning of missing an event may be completely different from a system where a simple and cheap repair can be performed. ## AI Within RAMS In the worlds of Reliability, Availability, Maintainability and Safety – RAMS, AI can add another layer of information analysis. But it does not replace the foundations of engineering. Reliability metrics, failure analysis, maintainability, availability, risk management, and safety still require models, requirements, and professional judgment. The interesting possibility is to connect the two: RAMS engineering provides the context and meaning. Data analysis helps identify patterns within large amounts of information. ## So, Is It Possible to Detect the Next Failure? In some cases, it is possible to identify early warning signs, estimate deterioration, or point to unusual behavior before failure. In other cases, the data will not be sufficient, the failure mechanism will be too sudden, or conditions will be different from what the system has known in the past. Therefore, the promise is not: "AI will know when the system will fail." The more professional approach is: "We will use data to identify situations worthy of inspection earlier and make better engineering decisions." ## In Summary The combination of AI and reliability engineering opens interesting possibilities for detecting anomalies, analyzing historical information, condition-based maintenance, and learning from failure events. But the technology does not stand on its own. To extract value from it, high-quality data, an understanding of failure mechanisms, a verification process, and professional engineering judgment are required. The future of failure analysis is not necessarily AI replacing the reliability engineer. It may be a reliability engineer who gets a better picture from the data – sooner. ### Want to connect system data to reliability engineering and RAMS? ALD Advanced Solutions supports organizations in the fields of reliability engineering, RAMS, failure analysis, FMEA/FMECA, FRACAS, and engineering risk management. Want to delve deeper? CRE – Certified Reliability Engineer Course · AI in Quality Management For a professional conversation with the ALD team →