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In the past, conventional preventive-maintenance techniques were applied to reduce machine downtime and the loss of productivity that accompanies unscheduled outages. The introduction of Industrie 4.0 in 2011 came with optimized industrial solutions to ensure machine availability and eliminate downtime. Predictive maintenance and condition monitoring are key components of Industrie 4.0 that apply proactive measures to forestall breakdowns.
During the first industrial revolution, overseers walked through the factory floor to observe operators and the machines they used on an hourly basis to ensure proper operation. These observational tactics were generally done to ensure work was ongoing but with a little twist. Instead of observing the operators’ work, the overseer was charged with inspecting machine performance to spot potential breakdowns before they occurred.
Condition management borrows a leaf from the early monitoring processes but includes an extra dose of digitalization and data analysis to determine machine conditions in real-time and to accurately predict failure rates. Formally, condition monitoring is defined as the process of monitoring a parameter of a machine to discover changes that point to a fault or defect.
The parameter could be vibration or temperature, where significant variations could identify an imminent problem. For example, vibration data from a lathe or any machine that uses a tool bit can help with identifying significant changes in the machine setup as it goes through its operation. In a scenario in which historical benchmark data exists, vibrations that exceed the benchmark highlight a defect or underlying condition that will affect performance.
Condition management also involves monitoring the performance of the equipment within its immediate environment. The data or information obtained from the monitoring process can be applied in several ways. The machine user can leverage the captured data to create a more conducive environment that guarantees productivity, while the equipment manufacturer leverages the data to create upgraded versions of the machine.
The smart factory relies on data collection, automation, and the ability to make data-driven decisions in real-time. Condition monitoring provides support for multiple Industrie 4.0 concepts such as predictive maintenance, determining machine utilization, and data-driven productivity optimization among others.
The continuous monitoring of machine data to detect wear is an important aspect of predictive maintenance because it provides historical data. Predictive maintenance relies on historical data to predict when the next machine or part failure is expected to occur. The predicted occurrence is then leveraged to create maintenance and part-replacement schedules that eliminate unexpected outages.
Today, more than 79% of facilities rely on predictive maintenance to reduce downtime. According to Deloitte, the successful application of predictive maintenance increases productivity by approximately 25% and reduces equipment breakdown by 70%. All of this is accomplished through the data captured by condition-management initiatives.
The data-capturing technologies used to implement condition management also provide support for implementing smart policies. Industrial solutions for condition monitoring, such as IIoT, smart devices, and sensors are data-producing sources that also have edge computing capabilities. The ability to compute data at the edge ensures these solutions can be integrated into larger Industrie-4.0 frameworks. Smart industrial solutions also enable the collection of data and interconnectivity for legacy assets with outdated communication technologies.
Condition monitoring on the factory floor increases safety and provides facility owners with the ability to implement specific regulations. One such example is the need to continuously monitor the statuses of electricity boxes to meet the safety quota set by the insurance service provider. Instead of deploying personnel to monitor the equipment at intervals, a simple sensor can be installed to record thermal data in real-time, which then monitors the condition of the diverse assets on the shop floor. These sensors are integrated with notification systems that can send alerts to centralized monitoring platforms, thereby securing the overall safety and health of the workers and automating important safety measures in the factory.
The major benefits of implementing technology or digitally transforming industrial processes are the promotion of a safe working environment and increased productivity. Condition monitoring meets these two important criteria by ensuring that the equipment works at its optimal capacity.
The successful prediction of equipment breakdown also protects the factory operator from unexpected shutdowns which may lead to loss of profit. Take for example the use of forklifts, which are notorious for causing thousands of accidents across the factory floor. Monitoring the condition of a forklift will reduce accidents which arise as a result of poor braking or operational performance within an occupied factory floor.
Condition monitoring is a proactive approach to maintenance and repairs with the ability to reduce the cost that industrial enterprises spend on maintenance. According to a Mckinsey report, the application of predictive maintenance increases the lifespan of equipment by as much as 40%. This means that the shop-floor facility is guaranteed a significantly enhanced financial return from the production machinery.
Condition monitoring provides industrial solutions to eliminate downtime, optimize productivity, and improve safety on the factory floor. The application of condition management is dependent on digitally transforming the factory floor and highlights another important area in which digital-transformation technologies can be relied on.