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Production downtime and factory productivity are closely correlated, as a factory can lose up to 20% of its productivity due to downtime.
The most common cause of production downtime is a malfunction or breakdown of equipment. However, it is possible to reduce equipment failure and keep downtime low with a predictive maintenance strategy that uses the Internet of Things (IoT), cloud computing and analytics.
The collection of equipment and environmental data occurs through sensors. The data is used to predict and remediate equipment failures proactively. Over time, machine learning advancements can improve the accuracy of predictive algorithms and allow you to build advanced prediction models.
A study reveals that 46% of manufacturers fail to deliver services to customers due to an unexpected equipment failure. Unplanned downtime also leads to a loss of production time on a critical asset and hinders manufacturers' ability to service or support specific assets or equipment.
Unplanned downtime affects all industries, and its impacts extend beyond the financial for some. According to an article in Petro Online, a single, unplanned downtime in an oil refinery or petrochemical plant releases a year's worth of emissions into the atmosphere.
It is worth understanding what Internet of Things monitoring entails to grasp its implications for downtime. An IoT monitoring system consists of four elements:
The first step in IoT monitoring is collecting data from the physical environment, which requires sensors. Sensors have specialty electronics that sense inputs from the physical environment and convert them to data for interpretation by machines or humans. The inputs include heat, light, moisture, sound, pressure or electromagnetic fields.
Sensors collect the data and send it over the cloud for analysis. Several methods are available to relay the data, including WiFi, satellite, cellular, Bluetooth or a direct connection to the internet via Ethernet. The type of connectivity used depends on factors such as power consumption, range, bandwidth, and security.
3. Data processing
When the data reaches the cloud, it is processed by software. There are many software solutions available for different IoT use cases. The solutions analyze the data and present it to end users in an easily understandable format. For example, you can set up sensors to display equipment vibration and temperature data every three seconds. Or you can run sophisticated analysis on a massive amount of IoT data and trigger appropriate action.
4. User interface
The end user can receive the data through a web, email, or text notification. For example, your factory manager may receive a text/web/email alert when the temperature sensor reading exceeds a certain threshold. The manager can then remotely adjust the temperature from their web or mobile app or trigger another remedial action that brings the temperature to a safe level.
IoT can be the key to minimizing downtime and keeping productivity levels high. Here's a discussion of the reasons for implementing an IoT-based predictive maintenance strategy.
1. You can monitor equipment in real-time
Real-time monitoring of asset condition and performance allows you to anticipate problems before they occur. Any maintenance required can happen moments after an alert, helping prevent a costly breakdown or any impact on plant performance. Timely maintenance is also helpful in maximizing the useful life of equipment — you can avoid having to replace equipment too soon and get the full return on your investment.
2. You can optimize the time taken to repair equipment
Predictive maintenance runs in the background, keeping you informed about machine condition and performance. You're alerted to deviations from optimal conditions, which tell you whether or how your equipment is aging or degrading. Using the information, you can accurately predict when the system is likely to fail and determine when to repair it.
As anomalies are relayed soon after they're detected, any issue with a machine is unlikely to go unnoticed and worsen. If deemed necessary, fixes in the early stage of equipment degradation won't take the hours usually associated with unplanned and planned maintenance.
3. You can spend less on repairs and parts
Predictive maintenance is data-driven and analytical, allowing you to get to the root cause of a problem rather than only treating its symptoms. Knowing what might lead to equipment breakdown is useful to prevent the wear and tear responsible for equipment failure. For example, alerts on suboptimal humidity help reduce the electrostatic discharge produced in a low humidity environment. Component degradation can be avoided, and equipment repair costs and spare parts inventory can be optimized to the desired level.
4. You can keep workers safe
Putting sensors in charge of detecting equipment issues bodes well for worker safety. For example, checking for bearing failure, a common cause of downtime, may require workers to access difficult or dangerous bearings to reach. With predictive maintenance, workers can check the bearings' condition without touching them. Smart sensors can gather information on the pressure and temperature of liquids flowing through pipes without requiring direct human intervention.
When to use IoT
It is useful for critical assets that have the greatest impact on production rate and profitability. IoT monitoring is also valuable when minute changes in environmental conditions can significantly affect product quality or worker safety. For example, sensors detect an operator's presence in a dangerous environment or faults in rotating machinery.
Data from IoT devices can be integrated with workforce solutions to develop work schedules that can reduce workers' exposure to hazardous conditions. As a passive safety solution, IoT can help enhance worker confidence and morale.
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