How predictive maintenance impacts preventive maintenance
You can’t shut down every time there’s an alert
- Continuously monitoring manufacturing assets through predictive maintenance leads to a much greater understanding of machine health.
- A multiple template approach helps an organization build predictive models with ease and incorporate machine learning and industrial AI to rapidly deploy them.
- An IIoT-based predictive maintenance system feeds data to the plant manufacturing execution system and enterprise resource planning setup, improving the performance of each and optimizing maintenance scheduling.
- Gathering data from CNC machine tools is a great first step when improving a predictive maintenance system and enables the creation of a unified predictive maintenance and machine health dashboard
- Start small, prove value, and scale fast!
Traditional preventive maintenance (PM) prescribes routine-based asset or system maintenance, regardless of whether performance has degraded or not. By establishing predictive maintenance (PdM), manufacturers can better define the optimum window of time when maintenance should be performed, based on the predicted future health condition generated from equipment data.
IIoT-based predictive maintenance feeds data into plant MES and ERP, to improve business performance.
MES manages manufacturing processes. Data derived from sensors and scanners may require manual data entry by operators and maintenance personnel. ERP primarily focuses on financial transactions tied to production, enterprise and supply-chain performance. Many ERP systems interface smoothly with MES, as opposed to competing or overlapping.
MES is essential to a plant predictive maintenance and scheduling. MES tend to be industry specific; an aerospace industry MES has different concerns than one for the beverage industries.
Combining predictive maintenance with MES ensures:
• An integrated system that allows correlating OEE and machine health
• Improved maintenance scheduling leading to better production scheduling and reduced unplanned downtime
• Integrated maintenance management, including predictive work orders.
A unified machine dashboard for health, maintenance and other key metrics, derives and distributes an optimal maintenance schedule.
Examples and case studies of predictive maintenance
From our experience developing predictive maintenance solutions, a 5% reduction in unplanned downtime can deliver $500,000 in annual savings for an automotive industry manufacturer customer and a 5-10% improvement in overall equipment effectiveness (OEE) saved an aerospace manufacturer $300,000 per year.
For a manufacturer looking to implement predictive maintenance, CNC machine tools are a great place to start.
The first step is to monitor front and rear spindle bearings. Vibration data can be measured by adding an accelerometer, typically installed on the spindle housing. Other data, such as spindle rotation speed and load or program number, are captured from the machine controller through protocols such as OPC-UA or MTConnect.
Controller data provides useful contextual information for understanding vibration data and leads to development of a corresponding predictive algorithm. Machine controller data alone is sufficient enough to build the model.
Important model parameters include position, speed and load of the actual axes, as well as the corresponding commanded values for those parameters. In addition, we focus on detecting pre-load loss and poor lubrication of linear axes or ball screws.
To enhance predictive maintenance with predictive scheduling, additional data from MES and ERP is needed. This data can be categorized as either dynamic or static.
Dynamic data, such as the maintenance and production schedule is gathered from the MES, including start time and end time for each activity and the production and operation IDs. Meanwhile, static information is collected from ERP, including spare part and shipping costs, supplier-provided lead times when ordering parts, as well as repair and downtime-associated costs. These are needed to determine an optimal maintenance schedule that takes heed of predictive maintenance alerts of an impending failure.
Enhancing predictive maintenance with predictive scheduling results in a unified dashboard showcasing machine health plotted against scheduled production orders. It also displays actionable information, such as when to order spare parts and when maintenance is required.
Through continuous machine monitoring, orders change from green to yellow as the urgency to replace or repair critical parts builds. With an integrated predictive maintenance and scheduling, maintenance not only uses machine health information to predict failure, but the team can now consider the risks of completing scheduled production orders versus accomplishing maintenance tasks.
Predictive maintenance and monitoring can make an existing computer maintenance management system (CMMS) more effective, on a per asset basis or across the enterprise. Machine parameters and sensor data pulled for each machine’s dashboard and the master health, predictions, and diagnosis reports can be pulled into a master CMMS dashboard.
If machine warning thresholds are close to being exceeded, the predictive maintenance system can automatically generate work request forms documenting asset, logged-in operator making the request, diagnostic needing attention, asset status (working and needing inspection or not working and needing repair), as well as detection and work-request dates and times.
The potential of data-based predictive maintenance saving hundreds of thousands of dollars per year may inspire some to think big, but many times it is more efficient to start small, prove value and scale fast. An effective strategy covers:
- Assessment: Identify a project, assemble a team and assess the digital maturity of existing process and participants.
- Documentation: Confirm the existing data-collection strategy, document the business case and calculate return on investment.
- Analysis: Ascertain the ideal end state for the project, its people and technology.
- Statement of work: Choose the components (hardware, software, sensors and software) needed for implementation and estimate the time required.
- Implementation: Build the templates, conduct the project and compile achievements.
- Scale: Identify lessons learned and build a center of excellence around the project to identify opportunities to repeat the success in other departments or plants.
Proving value by thinking big, starting small and scaling fast moves a plant’s productivity, maintenance and scheduling from a hope-for-the-best approach to a can’t-fail approach because data is flowing, and obstacles are identified before they become problems. You are now working in a smart factory.
A look at the predictive landscape
Predictive maintenance can serve as the eyes, ears, tactile sense and collective experience of the operator, shift supervisor and maintenance department.
The maintenance staff traditionally intuited when something didn’t look, sound or smell right. When something didn’t “seem” right, sure enough, many times it wasn’t. In other cases, fail-and-fix was the model. The results were unexpected downtime, upset customers, damage control and run-ups in costs.
Think of the Industrial Internet of Things (IIoT) as the convergence of information technology (IT) and operational technology (OT). At the bottom of the pyramid are the shop-floor devices (CNC machine tools, welding robots, conveyors and more) able to convey data to the cloud. The next layer up is a supervisory control and data acquisition (SCADA) system for obtaining data from sensors or machine controllers. To engage the business, the data may be fed into a manufacturing execution system (MES) and the results shared with the enterprise resource planning (ERP) system to improve data capture and applied analytics.
Innovations in condition monitoring
True predictive maintenance based on machine data and analytics can lead to near-zero downtime, reduced spare parts inventory and more effective maintenance through more accurate mean time between failures (MTBF) and mean time to repair (MTTR).
Continuously monitoring manufacturing assets for improved predictive maintenance leads to greater understanding of machine health, performance and failsafe production scheduling. A successfully implemented multiple-template approach enables an organization to build predictive models with ease, as well as incorporate machine learning and industrial AI to rapidly deploy them. Such templates involve:
- Develop models by collecting data from machine tools, robots, pumps, conveyor motors and other systems to develop data models
- Identify features for use
- Select and integrate machine learning and AI methods
- Establish repeatable analysis flow charts
- Deploy solutions rapidly and monitoring systems continuously for improved predictive maintenance and production scheduling.