We’ve been talking about predictive maintenance for quite some time. In our previous blogs we’ve explained what predictive maintenance exactly is, as well as what distinguishes it from reactive and preventative types of maintenance. In this article, we will be focusing more on the benefits that it offers to the industry.
To summarize our description from before, while reactive maintenance focuses on fixing the issue after it occurs and preventative involves fixing equipment at regular intervals, predictive maintenance uses sensors collecting data in real-time and analytics to predict when a problem might occur. It generally consists of five components: sensor, edge gateway, connectivity protocol, IoT platform, and analytics.
Sensors are used for data collection from the machines and below we can find the list of different measurement dimensions with associated examples.
- Vibration: measurement of machine’s vibration during operation, recognizing an increase in vibration as a potential risk
- Temperature: measurement of lubrication temperature, cooling water temperature and bearing temperature
- Oil particles: measurement of particle contamination levels
- Flow rate: measurement of cooling water flow and lubrication flow rate
- Current: measurement of the electricity consumption by machine components
- Humidity: measurement of water content in hydraulic and lubrication oils
IoT gateway is the next component of the process which collects this data as reported by the sensors, helps perform basic data processing at the edge, and transmits it to the IoT platform.
The cloud-based IoT platform acts like a data aggregator and gathers all the information into a data lake including structured, semi-structured, and unstructured data.
Finally, the analytics solutions help to make sense of the collected raw data. They analyze patterns and anomalies from past and present datasets and extract relevant data. Upon the completion of this process, you are ready to build on the insights generated.
After shedding some light on how predictive maintenance works, we will now be outlining some of the benefits that it offers:
Reduction of maintenance costs
As we mentioned above, predictive maintenance eliminates excessive actions when maintaining technical equipment and saves on wasted capital for unnecessary repairs. Additionally, it shows the exact faulty part that needs fixing. For instance, when a technician sees that a machine shows signs like increase or change in vibration – he doesn’t bother to go and search for particle contamination. He knows where and what the problem is which helps to fully focus his efforts onto problem resolution.
Reduced machine downtime
Machine downtime is a serious concern for manufacturers, especially for those, whose competitive advantages are built on lean production and on-time delivery. Downtime is the period which begins with a machine/ process failure and ends when it starts working again. During downtime when the specific machine is in a state of maintenance, it cannot perform its tasks – to put it simply – it is unusable. While preventative and reactive maintenance cannot do anything to speed up this process, predictive can. Because of the regular monitoring of the machines, not only can a failure be predicted, but one can recognize the faulty equipment which triggered the failure in the first place and take remedial steps early.
Reduced spare part inventory
Spare parts are needed when a machine breaks down unexpectedly and the technicians need to replace a faulty or broken part to continue operations. In the case of predictive maintenance, due to the effective assessment of the fault, manufacturers know exactly what parts they will need to purchase and in what quantity, as they can see which part of the machine has the highest possibility of breaking. This helps eliminate additional costs which are incurred with stocking up on spare parts.
Increased product quality
In a lot of the cases, product quality is directly connected to faulty production equipment. While quality is more difficult to detect when using reactive and preventative, predictive maintenance’s model has the advantage of using sensors to alert about any and all risks that the equipment is facing. Minimizing machine downtime and breakdown also improves the production process and thus – product quality and customer satisfaction.
Verification of repairs and human safety
Early detection of problems can alert human operators about the risks surrounding a specific equipment – this limits the potential and actual cases of work injuries. Additionally, after repairing the machine, the same sensor data and analytics solutions can be used to assess whether the implemented changes have resolved the forecasted equipment problems.
Although predictive maintenance is very beneficial in cost-cutting, it does require a significant amount of investment beforehand. Additionally, it might require some time to make the strategy sustainable and stable. If you are interested in the topic of predictive maintenance, you should take a look our study in collaboration with T-Systems – Customers´ Voice: Predictive Maintenance in Manufacturing, Western Europe, where you can find the results of over 300 expert interviews and deeper information regarding the topic. We will also be conducting a lecture on the topic at the 2018 edition of the Hannover Messe. Make sure to visit the Automation Forum at Hall 14, Stand L19 on the 27th of April between 10:40 and 11 AM to hear Lead IoT and Frenus’ Site Manager Bulgaria – Marcel Blume, speak about status quo, customer needs and decision paths regarding the adoption and potential of predictive maintenance technologies.