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Production downtime and factory productivity are closely correlated, as a factory can loss up to 20% of its productivity due to downtime.

The most common cause of production downtime is equipment failure or failure. However, it is possible to reduce equipment downtime and keep downtime low with a predictive maintenance strategy that leverages the Internet of Things (IoT), cloud computing, and analytics.

The collection of equipment and environmental data is done by means of sensors. The data is used to proactively predict and resolve equipment outages. Over time, improvements in machine learning can improve the accuracy of predictive algorithms and enable you to build advanced prediction models.

Related: How Cloud Agnostic Hardware Could Be the Future of IoT

Why minimize downtime?

A study shows that 46% of manufacturers failure to provide 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 maintain or support specific assets or equipment.

Unplanned downtime affects all industries and, for some, its consequences extend beyond the financial ones. According to an article in Petro Online, a single, unplanned downtime an oil refinery or petrochemical plant releases emissions into the atmosphere for a year.

Why does predictive maintenance use IoT?

It’s worth understanding Internet of Things monitoring to understand its implications for downtime. An IoT monitoring system consists of four elements:

1. Sensors

The first step in IoT monitoring is to collect data from the physical environment, which requires sensors. Sensors have special electronics that detect input from the physical environment and convert it into data for interpretation by machines or humans. The inputs include heat, light, moisture, sound, pressure or electromagnetic fields.

2. Connectivity

Sensors collect the data and send it to the cloud for analysis. Several methods are available to pass the data, including Wi-Fi, 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 easy-to-understand format. For example, you can set up sensors to display the equipment’s vibration and temperature data every three seconds. Or you can run advanced analytics on a massive amount of IoT data and take appropriate action.

4. User Interface

The end user can receive the data via web, email or SMS notification. For example, your plant manager may receive an SMS/web/email alert when the temperature sensor reading exceeds a certain threshold. The manager can then adjust the temperature remotely via their web or mobile app or initiate any other corrective action that brings the temperature to a safe level.

Related: 4 reasons to be excited about the ‘Internet of Things’

What is the role of IoT in reducing production downtime?

IoT can be the key to minimizing downtime and keeping productivity high. Here’s a discussion of the reasons for implementing an IoT-based predictive maintenance strategy.

1. You can track equipment in real time

Real-time monitoring of asset health and performance allows you to anticipate problems before they happen. Any maintenance required can be done immediately after an alert, avoiding a costly failure or impact on plant performance. Timely maintenance is also helpful to maximize the useful life of equipment – you can avoid the need to replace equipment too soon and get the full return on your investment.

2. You can optimize the time it takes to repair equipment

Predictive maintenance runs in the background, keeping you informed about machine condition and performance. Alerts you to deviations from optimal conditions, telling you if and how your equipment is aging or deteriorating. Using the information, you can accurately predict when the system is likely to fail and determine when it needs repair.

Since anomalies are communicated quickly after they are detected, a machine problem is unlikely to go unnoticed and worsen. If deemed necessary, repairs in the early stages of equipment degradation will not take up the hours usually associated with unscheduled and scheduled maintenance.

3. Spend less on repairs and parts

Predictive maintenance is data-driven and analytical, allowing you to identify the cause of a problem rather than just treating the symptoms. Knowing what can lead to equipment failure is helpful in avoiding the wear and tear that is responsible for equipment failure. For example, warnings about sub-optimal humidity help reduce the electrostatic discharge produced in a low-humidity environment. Component degradation can be prevented and equipment repair costs and spare parts inventory can be optimized to the desired level.

4. You Can Keep Employees Safe

Loading sensors to detect equipment problems bodes well for worker safety. For example, when checking for defective bearings, a common cause of downtime, workers must access hard-to-reach or dangerous bearings. Predictive maintenance allows workers to check the condition of the bearings without touching them. Smart sensors can collect information about the pressure and temperature of fluids flowing through pipes without the need for direct human intervention.

When to use IoT?

  • Reduce unplanned downtime
  • Reduce machine repair costs
  • Improve employee safety
  • Shorten the time to repair machines
  • Enable better use of equipment
  • Increase equipment ROI

It is useful for critical assets that have the greatest impact on production speed 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 the presence of an operator in a hazardous environment or malfunctions in rotating machines.

Data from IoT devices can be integrated with workforce solutions to develop work schedules that can reduce worker exposure to hazardous conditions. As a passive security solution, IoT can help increase employee confidence and morale.

Related: The ‘Internet of Things’ Is Changing the Way We Look at the Global Product Value Chain

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