Internet of Things connected devices generate a lot of data. The rise of machine learning and artificial intelligence help to make sense of the data, use it to analyse, make improvements. One way these forces are combining, particularly in the industrial sector, is the idea of predictive maintenance. Predictive maintenance takes advantage of the generous data output to identify where small issues are arising in machinery or processes. This can flag minor issues before they become serious and costly. In the past, most businesses were forced to use a ‘run to failure’ model. That is, machinery was deployed until it broke or malfunctioned. Only then could the issue be addressed. Breakdowns were unpredictable, difficult to prevent and often expensive to fix, especially if the failures caused follow-on damage to other parts, or delayed production. This guide explores how predictive maintenance can be used in the framework of the industrial Internet of Things.
What is predictive maintenance
When IoT connected smart devices generate data in real time, companies are able to deploy cloud-based analysis to monitor equipment integrity. If there are anomalies (such as additional vibrations or other signposts), the machines can be inspected and maintained before a catastrophic failure occurs. This can reduce downtime and safety concerns, which leads to reduced costs for the business.
Long life machinery behaves differently depending on the age and wear of the machine. Often, newer machinery can be prone to failure and need repairs as initial learning and trials runs occur. As the machine settles and the workers become familiar with its maintenance and service requirements, less breakdowns occur. This plateaux in servicing may stand until the machine gets closer to it’s end of life span. As the machinery ages, different maintenance may need to be carried out. Metal parts may become structurally weaker after repeated use, for example. These changing maintenance schedules can be modelled using industrial IoT modelling. Increased monitoring, decreased workload and preventative replacement can all help to avoid unpredicted failures.
There are some cases in which both industry and consumers can benefit from predictive maintenance technology. For example, many modern cars have onboard computers that analyse data instantaneously. The data is generated, analysed, actioned and crucially, discarded. Industrial IoT software connections have the potential to capture this data instead of discarding it. If a data aggregator can be connected to the internet using a sim card, incredible amounts of data can be captured and used to foresee maintenance issues, performance reports and suggested ways to improve efficiency and economy. The feedback loop can be accessed by a garage mechanic and the consumer. When a car is taken to a mechanic, a data reader can be connected to the onboard computer to get a report. When at home, the owner should be able to access a consumer-level data reader. This can help the consumer attend to minor maintenance that avoids larger issues and can help the mechanic manage customer flow by alerting them to upcoming needs based on reports. Finally, this data could be collected and anonymised, then fed back to car manufacturers to encourage them to build better cars over time, and possibly to spot model-wide trends that are not noticeable on a garage or consumer level.
The most significant way industrial IoT data analysis is changing industry is in the arena of identifying trends. Sometimes, smart machinery will generate alerts to their operators. When the reports are followed up there is sometimes no visible cause – these alerts can seem like false alarms or be dismissed as bothersome. On a case by case basis, they may amount to nothing, however, if multiple machines are able to report the same fault and AI and machine learning can identify patterns, there may be a larger problem unfolding. In this case, it is far easier to roll out a patch or adaptation or replacement or service to each of the machines, rather than waiting for significant errors or damage to occur.
Predictive maintenance can save lives
Predictive maintenance can be used in various industries. One utilities company is using drone technology in an effort to increase safety and improve service delivery. Drones are being deployed to visually map power lines and networks. Machine learning analyses the images and recognises trees that are in danger of falling on the lines. The trees can be removed or trimmed to reduce the risk. While the investment in equipment and data analysis may be significant, it has reduced the amount of service disruption, emergency response team costs and customer dissatisfaction.
Disadvantages of predictive maintenance
There are diverse use cases for predictive maintenance using an industrial IoT model. There are potential drawbacks and issues that can arise with using this technology, although it is our position that they are outweighed by its benefits.
- Data can be misinterpreted, leading to false maintenance requests,
- It’s costly to establish a complete IoT system with sensors, transmission costs and analysis,
- Predictive analysis may not take contextual information into account, such as equipment age or weather,
- Predictive maintenance may discourage proactive physical inspection and equipment maintenance,
- Preventative maintenance activities may be triggered by timelines rather than genuine machine condition.
Overall, using cloud-based predictive maintenance has been shown to regularly reduce overall costs. As with most nascent technologies there can be a period of transition, during which a dual maintenance system may need to be implemented. As the IoT model becomes established, a maintenance handover can be achieved with great results for outcomes and the company bottom line.
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