Maintenance procedures in industrial manufacturing facilities undergo rapid changes thanks to the application of connectivity, data collection, and artificial intelligence for predicting possible problems and malfunctions of machines.
Conventional maintenance procedures may be either based on predefined time intervals or repair actions. Machines are serviced according to a set schedule irrespective of their state, or fixed when a malfunction happens. In both cases, the process may be quite costly, result in delays and inefficiency of maintenance actions.
There is an alternative way – AI-based predictive maintenance procedure.
From Timed Maintenance to Condition-Based Approach
Data used by AI systems for maintenance procedures may be collected through different sources:
- Sensors
- Machines
- Systems
They may include information like:
- Temperature
- Vibrations
- Pressure
- Consumption of energy
- Noise level
- Speed of operation
- Utilization of equipment
- Maintenance history
Thus, artificial intelligence may detect various patterns indicating possible malfunctions.
Instead of asking about the schedule of maintenance of equipment, teams may ask:
What is the condition of the equipment and what is the optimal moment to take actions?
Identification of Early Warnings with the Help of AI
Many equipment breakdowns are accompanied by small changes in their performance that may not be visible during a routine check, but can be spotted after analyzing the huge amount of operational data over some period of time.
With the help of AI, it is possible to compare current performance of equipment with previous one and spot deviations. For instance, increased vibration levels accompanied by elevated temperatures are indicative of possible mechanical problem.
The system will raise an alarm, set the priority of the problem and give necessary information for maintenance staff.
This does not mean that each alarm will require repair right away.
How IoT Can Be Used in Predictive Maintenance
For AI to work, it needs data, and the IoT devices supply most of the data needed for predictive maintenance.
These connected sensors will allow equipment to send data either to local systems or to cloud computing environments, which will enable storage and processing. This is how a feedback loop will be created between hardware and software.
In general, the process of predictive maintenance can be summarized as follows:
Connected Equipment –> Sensor Data –> Data Platform –> AI Analysis –> Risk Alert –> Maintenance Activity
The main point is to connect all these steps together. Without proper connection between the sensor data and the subsequent processes, such information alone is useless.
Cost Reduction and Prevention of Downtime
Downtime can be extremely costly, especially in the manufacturing sector, power generation, logistics and other capital intensive industries.
Predictive maintenance can help organizations cut costs related to unplanned downtime in various ways.
Some of them are listed below:
- Less unexpected malfunctions of equipment
- Fewer interruptions in production
- Proper spare parts planning
- Cost savings on emergency repairs
- Increased availability of equipment
- Equipment lifetime increase
- More efficient maintenance planning
Nevertheless, there are ways to quantify these potential advantages since organizations can calculate downtime in hours, maintenance expenses, mean time between failures, mean time to repair, and equipment availability both before and after implementing predictive maintenance.
Optimizing Maintenance Workflows
There is more to predictive maintenance than just identifying failures.
When maintenance specialists receive more detailed information about what needs fixing, they can equip themselves properly for the task at hand. This can help prevent returning to the equipment to fix things for the second time.
Predictive maintenance systems can also help prioritize work orders depending on risks, production impact, and asset criticality. In other words, this technology helps teams allocate efforts where they can bring the most benefit to operations.
Things to Consider
The implementation of predictive maintenance is not only about setting up sensors and selecting a proper model.
Organizations need to consider:
- Data quality
- Sensor reliability
- Connectivity
- Cybersecurity
- Integration with maintenance systems
- Model accuracy
- Training
- Change management
- False alarms
- System monitoring
However, bad data might result in inaccurate predictions. An excessive number of erroneous alarms will eventually lead to alert fatigue. On the other hand, if the system does not give any warnings, the trustworthiness of the system will decrease.
That is why the implementation of the solutions of predictive maintenance must include well-defined goals, adequate testing, and constant feedback of maintenance engineers.
Expert Knowledge Remains Important
AI can recognize patterns and generate recommendations, but experienced engineers continue to play an important role.
Maintenance engineers know how the equipment behaves under certain conditions and are aware of some issues that do not always exist in historical data.
The best practice is the combination of machine and human intelligence.
Practical Industrial Solutions With AI
There are lots of practical examples of using the intersection between AI and IoT for connecting digital processes with physical processes.
At Aperture Venture Studio, our goal is creating industrial solutions with AI and IoT technologies that can provide business value in operational industries. And the thing is not about collecting more data but using it for making decisions and creating efficiency and business impact.
Predictive Maintenance in the Future
The future development of predictive maintenance will probably include models of failures detection and edge computing. Besides that, the future systems of maintenance may combine information about failures with production schedule and some other information, such as environmental factors, personnel, inventory, and etc.
The best future system is the one that integrates well with existing workflow and provides business value.
Predictive maintenance is not only about the ability to forecast breakdowns of your equipment. It gives you an ability to plan and react in time.
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