Smart Maintenance Management
The Internet of Things (IoT) is a way to connect devices, collect data, transform it to information and distribute it through the organization via internet (cloud) technology. How can we incorporate and make the best use of IoT in our day-to-day maintenance and asset management activities? Moreover, how can we link machines, technology and humans together to create an innovative and intelligent maintenance system?
Due to the Internet of Things, business models will change. Ageing assets, relocating production facilities, a shortage of qualified technical personnel and changing customer demands. For example, one-piece production or paying for the use of machines instead of paying for the whole machine. This, combined with increasing technological innovations, will shift the way equipment suppliers, production facilities and end-users interact. It raises more awareness on efficiency, ease of use, flexibility, maintenance needs and, of course, quality of the equipment.
The biggest shift to be seen in maintenance models in the coming years will be the change from corrective to predictive and condition-based maintenance. And even in the maturity level of predictive maintenance there will be a shift from fixed time-based maintenance to adaptive maintenance based on real-time and historical data.
Intelligent maintenance & asset management system
Due to new technologies and easy access to the Internet or working in the Cloud, accessing and remotely monitoring machines can be done anytime and anyplace. This asks for reliable remote monitoring and access. Safety issues also play a big role in adopting new technologies using the Cloud or Internet connections.
Depending on the criticality of equipment, some equipment needs near to real-time condition monitoring to ensure it will not fail causing great losses or production stops. Critical equipment has a risk profile indicating that the consequences of failure are severe. Condition monitoring, diagnostics and remote access require an intelligent system to bring together the extended amount of data and analysis that is needed in this next level of maintenance management.
The aggregation of data collection, storage, analysis and decision making for smart maintenance is called an intelligent maintenance system. Monitoring and analyzing the behaviour of machines and components of machines has become possible by means of advanced sensors, vibration analyses and other smart technologies.
An intelligent maintenance system can process the collected data, provide insights in behaviour, anticipate erroneous situations, trigger alarms and give instructions for preventative maintenance. It combines Big Data and the Internet of Things, collecting data related to historic events, the asset health, performance of machines and machine parts and can thus effectively reduce operational and maintenance costs.
IoT enables maintenance systems in remote monitoring in a much wider range of devices and machines than was previously possible. With an intelligent maintenance system organizations can gain valuable insight in machines and specific components of machines.
Unplanned downtime can be eliminated by the automatic scheduling of maintenance before equipment failure happens, and when it does happen you will know exactly where and how to solve it. Total availability and performance of equipment can be improved.
With an intelligent system, standard supplier data is matched to behaviour patterns so that optimal maintenance can be planned. In time this leads to an optimal reliability and maintenance & asset lifetime strategy.
Making maintenance management smart
An intelligent maintenance system brings together technology, data, analyses, prognosis and resources aiming for the machines and systems to achieve highest possible performance levels and near-zero breakdowns.
To make the most effective use of an intelligent system you first will need to think about which data you want to collect and how. With the right data acquired from monitoring assets, companies can obtain a competitive advantage by gaining relevant insights in the performance and usage levels of machinery. By using an intelligent system criticality, planning, processes, production, use of resources, maintenance, stock levels and spare parts can be managed to an optimal form.
Data collection and remote monitoring combined with an intelligent maintenance system enables an information-based and future-oriented predictive and optimal maintenance strategy. Prognoses and valuable insights can be created on the future, actual and historical condition of machines and machine components.
The ultimate goal of using an intelligent maintenance system, leading to predictive maintenance, is to perform maintenance at a set point in time when the maintenance activity is most cost-effective and before the machinery loses performance within a threshold of time.
Also of great value is the opportunity to collect an entire database with diverse knowledge on certain parts, machines and suppliers. Knowing how equipment holds under different circumstances and usage level. For example, the production environment, running time, what is processed, temperature, humidity, operation of the machine and so on.
Because machines and parts are monitored for each machine individually, they are not based on average data from other machines or supplier specifications. This has the advantage that the individual performance curves, circumstances in production environment, and the operational strategy and if necessary, previous data on historical incidents can be included in the analyses.
Machine builder case
Together with an international operating machine builder we started a pilot project combining sensor data, M2M communication and an intelligent maintenance software. The goals were to get a better insight into machine life cycle, machine and individual components behaviour, maintenance management needs and possibilities for remote monitoring and diagnostics.
For the machine builder, the main questions were how do our machines and specific components perform under different production and environmental circumstances, processing of different substances (liquids, granulates and so forth), general usage, running time. And accordingly, how can we give our clients the best possible service and advice on maintenance management so that the full potential and performance capability of our machines is reached.
We started with a pilot project with a specific type of machine. Sensors were placed on the machine to measure variabilities such as environmental data and the type of product processed by the machine.
This data was sent to our database using a remote machine to machine solution with a one-way data sending option. This was used to secure the data and external access to the machine.
We took the raw data and turned it into valuable insights for both end-user and our machine building partner. Sensor data, control data and environmental circumstances were connected to our intelligent maintenance and asset management system. With all of this real time information about the health and performance of equipment, valuable insights were gained for future design, usage, performance levels, service & maintenance management and new business models.
Food processing company case
Together with an international operating food company we started a project to change maintenance management from corrective to condition-based, and even for some critical assets, predictive maintenance management.
In this case, vibration analysis, combined with other data collected from intelligent remote devices was processed and analyzed in an intelligent maintenance system. The result was the creation of intelligent decision support tools and an insight into required actions and critical resources.
The future of maintenance and asset management in today’s rapidly changing and increasingly technology-oriented environment depends heavily on taking advantage of a new breed of technology and software that answers not only the question of what assets do I have, but also the question of how do I maintain them. The future tasks will be monitoring and knowing the health of your assets, planning maintenance when you anticipate it is really necessary and improving performance and effectiveness of resources. Making maintenance smart.
We have all read about it: leak detection should be a top priority since, if no leak detection program has been implemented, leaks can account for 30 to 40% of consumed volume. So, why is this issue still on the table? Why is it difficult to change things in the field?
A growing number of companies want to use big data analytics in their predictive maintenance and are also investing in the resources needed for this. Of the companies already using this technology, no less than 95 percent say that they have already achieved concrete results. This is the conclusion of a follow-up study conducted by PwC and Mainnovation among 268 companies in the Netherlands, Germany and Belgium.