Manufacturing industries manage complex production environments where even a single hour of downtime caused by unreliable or unavailable assets can cost the manufacturer millions. Not to mention, there are added risks of hazardous leaks, life-endangering accidents, and a complete breakdown of the value chain. One of the reasons behind the uptake of Industry 4.0 technologies and systematic digital transformation is the need to overcome this inherent uncertainty in discreet and process plants.
Intelligent automation and advanced analytics can enable operation and maintenance teams to improve asset availability in their plants. Furthermore, mission-critical assets can be made more reliable, helping plant teams achieve intended targets and objectives. This is why there’s an increasing shift in focus toward digital plant reliability and manufacturing leaders are prioritizing the adoption of technologies that can drive these objectives.
This article will cover what digital reliability is, what are its benefits, and which technologies are driving digital reliability in manufacturing industries.
What is Digital Reliability?
Reliability is the probability of a system meeting certain pre-defined performance standards and delivering desired output for an intended period of time. A reliable system continues to perform within specific parameters, without experiencing any anomalies or breakdowns and operates at optimum productivity level. Digital reliability is ensuring asset and plant reliability through smart digitalization of processes and increased data availability to support decision-making.
Contemporary digital reliability solutions rely on real-time condition monitoring of manufacturing equipment, performing predictive analytics on collected data, and mapping the machine performance to generate a realistic health status of the asset. Data irregularities are investigated to diagnose existing or potential faults, and take corrective measures to mitigate the risk of failure.
Take a steel manufacturing plant, for instance, where a cold rolling mill is critical equipment that controls the production flow and throughput quality. The asset is responsible for achieving greater dimensional accuracy and increasing the hardness of the final product. If a cold rolling mill is available but not functioning under optimal conditions, then potential breakdown can lead to several hours of production downtime. With digital reliability measures, this eventuality can be avoided and the cold rolling mill can become highly reliable, saving over 72 hours of production downtime. (Read the full case here.)
Predictive Maintenance for Digital Reliability
More than 70% of equipment breakdown is due to mechanical faults, including equipment wear, deterioration, backlash, increase in clearances, vibrations, and acoustics. While for hydraulics, thermal and electrical faults, standard monitoring solutions are available, for mechanical faults, monitoring becomes challenging. To drive digital reliability objectives in these scenarios, predictive maintenance becomes an important enabler.
With Predictive Maintenance (PdM), plant maintenance teams can estimate the exact remaining useful life (RUL) of the equipment perform accurate diagnostics, and receive insightful recommendations to strategically plan maintenance activities. Real-time monitoring of triaxial vibrations, acoustics, and surface temperature is utilized to generate digital reliability reports and guide maintenance schedules. Responsive predictive maintenance solutions can also:
- Adapt to special production conditions
- Diagnose high-frequency data
- Accurately decipher the signal from noise
- Overcome complex bandwidth limitations
- Collect and store multi-location data on-cloud
To Know more about Digital Reliability: https://www.infinite-uptime.com/understanding-digital-reliability/
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