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. EC-MSP’s predictive maintenance services may come in handy for SMEs, since we have been successfully providing outsourced IT support in London for years.
Maintenance schedules
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.
Industrial/consumer hybrids
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.
Spotting trends
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.
Should my business be using predictive maintenance?
The use of predictive maintenance technology has become one of the most integral systems in organisations’ IT security infrastructures. Incorporating predictive maintenance means that your IT devices and software remain protected against potential threats and cyber-attacks.
In addition to ensuring cyber security remains up to scratch, predictive maintenance also ensures that all necessary operational equipment stays up to date.
If you’re wondering whether or not your business should be using predictive maintenance technology, the answer is simply – yes. There are several benefits to integrating predictive maintenance into your business systems.
The main benefit is that it reduces the amount of downtime your company has to undergo when repairs need to be made, thanks to a system malfunction. When you implement predictive maintenance, you can catch problems early and mitigate risks before they impact your business and daily operations.
Thanks to the reduction in downtime, your predictive maintenance technology also helps you to reduce the amount of money your business spends on repairs and reduces the cost to your business spent during inactivity.
Although the upfront cost of predictive maintenance technology may seem like a lot to put down, the cost saving in the long-run will ultimately save your business money.
Conclusion
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. Managed IT services providers, like EC-MSP, have shown to be able to make this transition as smooth as possible for their clients. As the IoT model becomes established, a maintenance handover can be achieved with great results for outcomes and the company bottom line.
About EC-MSP, your IT support partner
EC-MSP are one of the most trusted IT support providers in London. If you would like more help advice and support with establishing an industrial Internet of Things cloud-based ecosystem, or any other IT support issues, contact us today to see how we can help.
