ndustrial machinery is the beating heart of modern production. Whether you are running a small-scale workshop or a fully automated plant, the moment equipment begins to falter, the clock starts ticking—downtime is expensive, sometimes ruinously so. A 2023 study published in Measurement found that unplanned stoppages cost manufacturers an average of £17,000 per hour in lost productivity and missed deadlines. The good news is that with the right fault detection strategies, many of these losses can be prevented before they even start.
Fault diagnosis is the process of identifying, isolating, and understanding the root cause of a malfunction in engineering plant and equipment. The practice combines structured standards like SEMETS355—a UK National Occupational Standard for carrying out fault diagnosis—with cutting-edge data-driven techniques that harness artificial intelligence and real-time monitoring. In other words, effective diagnosis is both a science and an art: the science of applying structured methods, and the art of interpreting signals, symptoms, and behaviour in complex systems.
The High Stakes of Getting Diagnosis Right
Imagine a bottling plant running 24/7 to meet seasonal demand. A vibration in one of the main conveyors goes unnoticed for a few hours. Without intervention, that vibration will wear bearings, overheat motors, and eventually halt production. If the plant manager had access to continuous monitoring and automated alerts, they could have called in a technician before a complete breakdown occurred.
That’s why industry standards stress early fault detection. The SEMETS355 framework specifies that technicians must not only locate the fault but also follow strict safety procedures, use approved diagnostic tools, and document every finding. This documentation isn’t just for compliance—it’s vital for future troubleshooting, trend analysis, and even training new engineers.
For example, at Hasuka Automation, the sourcing team regularly helps customers identify whether their breakdown is due to component wear, firmware corruption, or even environmental factors like dust or humidity. A well-documented diagnosis can cut replacement time dramatically by pointing engineers directly to the right spare part—whether it’s a PLC module, a servo drive, or an obsolete manifold valve.
How Fault Diagnosis Has Evolved
Historically, diagnosing faults meant relying on physical models—engineers would compare observed symptoms to known mechanical behaviours. If a pump vibrated at a specific frequency, it might suggest imbalance; if a motor overheated after a set run time, it could point to winding failure. While effective for simpler systems, this approach becomes impractical in complex automation setups with thousands of interacting parts.
The second wave of methods relied on signal processing—analysing patterns in electrical signals, vibration spectra, or thermal images. These techniques brought precision but required deep theoretical knowledge and labour-intensive setup.
Today, the third wave is data-driven diagnostics. Using machine learning algorithms, industrial control systems can detect subtle deviations from baseline performance—deviations invisible to human senses or traditional thresholds. For example, a predictive maintenance system might flag a gearbox for inspection two weeks before any audible noise appears, saving thousands in unplanned downtime.
Common Fault Diagnosis Methods in Industry
There is no one-size-fits-all method, but several diagnostic strategies are widely used:
-
Visual inspection – The oldest and simplest method, still relevant today, especially for spotting leaks, wear, or corrosion.
-
Performance monitoring – Tracking output metrics like cycle time, throughput, or energy consumption.
-
Vibration analysis – Detecting mechanical imbalances, misalignment, or bearing defects.
-
Thermography – Using infrared imaging to spot overheating components.
-
Ultrasound testing – Identifying leaks or electrical discharge.
-
Data analytics and AI – Leveraging real-time sensor data to predict and prevent faults.
According to a report by TWI Global, combining multiple methods yields the highest reliability. A hybrid approach reduces the risk of false alarms and increases the accuracy of locating the root cause.
The Cost of Waiting Too Long
Delaying diagnosis can be catastrophic. In one documented case from a European automotive plant, ignoring a warning on a robotic welding arm led to a chain reaction: the arm seized mid-production, damaging the conveyor line and halting work for 72 hours. The total loss exceeded £1.2 million, not counting reputational damage from late deliveries.
Even minor delays can have ripple effects. When a supplier can’t deliver on time because their production line is down, their customer’s schedule is also disrupted. This is why many plants keep critical spares in stock—or partner with companies that can source and ship replacements quickly, even for obsolete components. Hasuka Automation’s spare parts service is built around exactly this need, providing access to both current and legacy industrial automation parts.
Building a Proactive Fault Diagnosis Culture
Early detection isn’t just about tools—it’s about mindset. Plants that perform well in reliability surveys tend to:
-
Train staff to spot early warning signs.
-
Schedule regular preventive maintenance.
-
Use IoT and SCADA systems for live condition monitoring.
-
Keep clear historical logs for each asset.
For example, integrating vibration sensors into an existing PLC network allows maintenance teams to monitor trends remotely. A sudden spike in vibration amplitude can trigger an email or SMS alert, prompting inspection before the machine fails.
Looking Ahead: Fault Diagnosis in Industry 4.0
The future of industrial fault detection lies in predictive and prescriptive maintenance. Predictive systems forecast when a fault will occur; prescriptive systems go one step further by recommending the exact corrective action. In the coming decade, expect to see AI systems that automatically order replacement parts, schedule technicians, and reconfigure production schedules to minimise disruption.
However, technology is only as strong as the processes that support it. Combining structured frameworks like SEMETS355 with advanced analytics ensures that every decision is both technically sound and operationally safe.
Final Word
Industrial fault diagnosis is more than a technical skill—it’s a competitive advantage. The faster and more accurately you can detect and resolve equipment issues, the less money you lose, the safer your plant becomes, and the more confidence your customers have in your ability to deliver. For organisations looking to bridge the gap between traditional engineering know-how and modern predictive tools, the key is to blend people, processes, and technology into one coherent strategy.
Explore our blog for more insights into industrial automation, maintenance strategies, and sourcing rare components to keep your operation running without a hitch.
References
-
UK Standards – SEMETS355: Carrying out fault diagnosis on engineering plant and equipment. Retrieved from: https://www.ukstandards.org.uk/en/nos-finder/SEMETS355/carrying-out-fault-diagnosis-on-engineering-plant-and-equipment
-
Yang, G., et al. (2024). A roadmap to fault diagnosis of industrial machines via machine learning. Measurement, Elsevier. Retrieved from: https://www.sciencedirect.com/science/article/abs/pii/S0263224124021018
-
Niu, Q., et al. (2023). A fault detection method for industrial equipment based on multi-attribute decision fusion. Diagnostyka, 24(4), 77-88. Retrieved from: http://www.diagnostyka.net.pl/pdf-192496-115590?filename=A+fault+detection+method.pdf
-
Li, H., et al. (2024). Fault detection and diagnosis in Industry 4.0: A review on challenges and opportunities. Sensors, PMC. Retrieved from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11723332/
-
Zero Instrument. (n.d.). 10 Commonly Used Methods to Diagnose Industrial Instrument Faults—How Many Do You Know?. Retrieved from: https://zeroinstrument.com/10-commonly-used-methods-to-diagnose-industrial-instrument-faults-how-many-do-you-know/
-
TWI Global. (n.d.). Early fault detection for industrial machinery. Retrieved from: https://www.twi-global.com/media-and-events/insights/early-fault-detection-for-industrial-machinery
-
Measurement & Control Journal. (2023). Cost impact of unplanned downtime in manufacturing. Retrieved from: https://journals.sagepub.com/home/mac