While there has been a significant boom of smart manufacturing technologies and sensor-controlled shop floors, maintenance isn’t lagging in adopting digitization. To ensure longevity and optimized performance of machines and equipment, they demand timely maintenance. Digitization in maintenance offers a significant edge over traditional workflows to optimize overall performances.
Current Maintenance Workflows
Currently, many maintenance activities are unscheduled and unplanned. Maintenance throughout the industry runs on whims. It is driven by visual inspection with no specific approaches or defined methods/protocols.
Usually, a senior plant engineer is the one who is calling the shots. Maintenance is scheduled based on their experience and manual checks. But for heavy and large factories this is unsustainable in the long run.
This is where AI comes into the maintenance space, to analyze situations accurately. AI technology enables plant managers to make informed decisions by evaluating the complete picture through user dashboards. These dashboards show real-time charts and graphs of assets, collected from the shop through sensors and various data touchpoints.
The Need for AI in Manufacturing Maintenance
Smart factories are slowly changing the idea of maintenance. It has moved away from the traditional idea of merely a process to fix broken machines. Manufacturing maintenance has become a precautionary step to ensure meeting the output and taking care of anomalies before the equipment stops working. Proactive maintenance is the trend today, and many manufacturing companies see it as an opportunity to reduce overhead.
Connected networks and smart machinery have enabled gathering data to derive actionable insights. While it surely helps to schedule timely maintenance, digitized and connected shop floors also enable increasing Overall Equipment Effectiveness (OEE). And enterprises adopting it have seen promising results despite the higher initial cost.
If you are also looking for ways to adopt AI into your maintenance schedule, here are five necessary steps you must implement:
1. Changing the Approach and Outlook
The fundamental idea behind adopting AI is the core focus on profitability and productivity. And to make the most of AI, manufacturing enterprises must start by changing the mindset of maintenance supervisors. It demands a radical shift of mindset to move from random to regular.
For example, organizations should find out and monitor the Key Performance Indicators (KPIs) of critical assets. This information should be compared with standard values to monitor asset health and schedule maintenance on time.
Furthermore, manual maintenance is always inferior when compared to machine maintenance. For instance, robots ensure better output of cleaning the assembly line relative to manned equipment. FANUC and Epson are some of the leading manufacturers and deployers of industrial robots that also recommend this course of action.
2. Upgrading the Infrastructure with Data Connectivity and Sensors
IoT technology gives you the edge over traditional maintenance that you require to stay informed. Maintenance supervisors and plant heads can learn and predict breakdowns in advance. Installing sensors at strategic places with data connectivity, cloud and connected networks create a smart factory and help optimize overall plant performance.
For example, install vibration sensors on-grid and as close to the object under observation as possible (such as a shaft, an axle, or a bearing). These sensors will detect any abnormal vibrations and trigger an alert. Similarly, there are temperature sensors, proximity sensors, oil sensors, flow sensors, and more to measure and check various parameters of your assets and maintain them accordingly.
These sensors will send the data from the shop floor back to a monitoring screen for storage, analysis, and action. The key to the success of this entire setup is the optimum number and location of sensors to get sufficient data from the shop floor.
3. Collect, Transfer and Load Data
Once sensors are in place, the data sent by them is collected and stored in a data warehouse. Data scientists and data analysts use it to derive actionable insights for the C-suite and the managers. But the catch here—on top of collecting, transferring and storing it—is structuring it first.
Most of the time plants have humongous data sets, both in silos and unstructured. Even if they are structured, these data sets do not communicate, making them unable to derive intelligent results.
By structuring data, and getting results through intelligent queries using platforms like Hadoop or MySQL, plant maintenance supervisors are empowered with data-backed decisions. Dashboards can be created and updated in real-time to take timely actions and avoid any major loss.
Data collected over a substantial period of time, combined with a variety of datasets, helps build suitable algorithms and enables the implementation of machine learning techniques.
4. From Regular Checks to Regular Monitoring
As stated earlier, data from the shop floor enables monitoring in real-time. This means you don’t have to check for misalignment or wrong components. You get a notification stating that something is wrong. This way, you get to eliminate human error and improve product quality.
On top of that, a perfect monitoring system backed by AI also throws insights into the deviation of the standard tool path. This means that scrap can be reduced and accidents can be prevented, which is usually an issue with manual checks and inspections.
For example, a supervised ML program can detect excessive temperature on machine motors and send alerts; there’s no need to keep checking once the sensors are installed. AI leads to improved heat, light, motion, cooling, distance and other parameters needed for proper machine operation.
5. Avoidance, Not Fixing
Maintenance has historically included repairing something that is broken or fixing an unusual issue. This has, however, proven to be costly. AI drives predictive, preventive and prescriptive maintenance through deep, actionable insights from the smart shop floor.
The results of a survey by Senseye found that large facilities lose 27 hours a month to machine failures on average, at the cost of $532,000 for each hour of unplanned downtime.”
At the same time, there are individuals within the manufacturing industry that are against spending on maintenance programs. It can cause them costly downtime due to over-maintenance of assets costing $260,000 per hour.
Conclusion
A senior data scientist at RapidMiner, Scott Ganzer, says, “ML can run sensor data through a statistical model to detect conditions defined as corresponding to a developing defect.” Thus, AI and ML applications can go a long way to optimize maintenance project outcomes on the shop floor.
By connecting cloud-based applications, they also provide remote plant monitoring, which brings not only monetary benefits but faster actions as well. These data sets impart value judgment and help maintain your assets in the long run.
