Predictive monitoring helps businesses grow by reducing machine downtime. Every minute of downtime costs a company a significant amount of money. Historically, oil and gas companies needed help maintaining visibility into equipment in remote offshore places. Using sensors, predictive maintenance enables companies to monitor the condition of their assets.
Predictive maintenance is a process of identifying and predicting machine failures. It can help reduce downtime and improve worker safety. However, implementing predictive maintenance requires a large amount of data to be collected and analyzed. It can also be expensive. The industrial Internet of Things (IIoT), machine learning, and big data analytics are vital for implementing predictive maintenance. These technologies allow facilities to monitor the physical actions of machines and convert them into digital signals that can be analyzed for signs of deterioration or failure. The signals are then interpreted by an expert who plans service measures to be implemented in the future. Unlike reactive maintenance, predictive maintenance is proactive and prevents unexpected shutdowns. This translates into lower operating costs and longer equipment lifespans.
Asset management is a key component of any business that requires a comprehensive plan for the care and use of its assets. From forklifts to computer software, effective asset management can help businesses save time and money while ensuring compliance with regulations. Asset tracking systems work with predictive monitoring to provide a network of data that ensures machines operate within their optimum conditions for the best possible output. Whether it’s an alert for high temperatures or vibrations, these alerts can prevent costly downtime and loss of productivity.
Predictive analytics can identify a potential problem 60 or 90 days in advance, unlike traditional condition monitoring. This enables maintenance teams to schedule proactive repairs before the equipment is about to fail. This can cut the cost of maintenance by a significant amount.
A predictive maintenance program can improve productivity by reducing equipment downtime. It can also help to reduce operating costs. Moreover, it can prevent production delays by tracking changes in machine performance. For example, a sensor can alert the maintenance team to a temperature rise in a pump, which could indicate it is under stress and might fail soon. This allows the team to schedule an inspection and repair before the pump fails, which can halt production. Vibration analysis is a common type of condition monitoring used in manufacturing plants that use high-rotating machinery. It can detect looseness, imbalance, misalignment, and bearing wear. It is less expensive than other SHM tools and CM tools. It can also track environmental conditions. This data enables maintenance to be based on a more informed decision.
Often, a single-machine failure will halt production and cost the business money. With predictive monitoring, a team can reduce maintenance downtime by identifying impending issues and taking action before a failure occurs.
A common form of predictive maintenance involves using vibration analysis to detect looseness in equipment such as pumps or other gear. This approach is prevalent in manufacturing and energy plants with high-rotating machinery. Another method involves capturing and analyzing sensor data to identify trends. This method allows operations to move equipment up on the maintenance schedule based on the information it provides and prevents costly downtime and unscheduled work. This can save businesses more than 50% in maintenance costs. It also improves reliability and productivity. It can also help to keep people safe, especially in industries such as healthcare and energy.
Using predictive process mining tools, business leaders can optimize operations and enhance performance while mitigating risk. The tools can also speed up decision-making by delivering real-time insights.
Achieving greater profitability requires the ability to identify inefficiencies and improve production rates. Predictive monitoring alerts ops teams to equipment issues before they become major problems, saving on repair costs and production downtime. It can also help businesses manage their inventory better by predicting when a specific product will be delivered to customers. This can help enterprises to avoid overstocking or understocking, which can result in lost profits. In addition, the software can predict delays and inform customers about them, helping improve customer satisfaction. This is called prescriptive process monitoring or mining and is an important capability that some predictive analytics vendors are developing.