Any values reported by or measured at a manufacturing machine are machine information. This information might describe what the machine is doing, particular course of variables, output information, high quality information, or information concerning the setting throughout the machine or the situation of the machine itself. Machine information assortment additionally entails information held on different methods used to handle the plant.
Sorts of Machine Knowledge
Machines geared up with PLCs can acquire information about processing situations or the work carried out. Machines will also be instrumented to collect information about their situation. There are a selection of alerts and information a machine information assortment system can collect, equivalent to the next.
Sensor information
A few of the industrial machine sensors used for course of management can additionally seize machine well being information. Examples are temperature, vibration, present draw, and oil situation. More and more, machine builders are utilizing “good” sensors the place edge computing capabilities filter or course of information earlier than transmitting it over a community.
Log information
This sort of information quantifies what the machine has accomplished and is usually retained on a PLC or in PCs. It contains machine manufacturing information equivalent to portions processed, cycle occasions achieved, yields or measurements, and different inspection outcomes. It will probably additionally cowl operational data equivalent to run time and downtime.
Community information
Some trendy gear comes with IoT sensors put in that may ship information to different methods such because the CMMS. In different instances, producers retrofit machines with IoT units. Knowledge is shipped over wired Ethernet, Wi-Fi, Bluetooth, and even mobile networks when longer vary is required.
Many course of vegetation function SCADA (supervisory management and information acquisition) methods the place instrumented gear shares standing data with a central controller. This may very well be course of parameters equivalent to temperature or gear situation, e.g., whether or not a valve is open or closed.
Why Is Machine Knowledge Assortment Vital for Producers?
Manufacturing machine information provides visibility into what’s occurring within the manufacturing facility and might present insights into gear operation and efficiency. Makes use of embody the next:
- Predictive upkeep: Machine situation information can drive a upkeep technique the place work is completed simply earlier than it turns into essential. This reduces upkeep prices whereas additionally enhancing machine availability.
- Yield enchancment: Course of information reveals sources of variation that lead to merchandise being exterior of specs. This may drive enchancment efforts to seek out and cut back waste.
- Growing utilization: Changing information on whether or not a machine is working, being modified over, ready, and even not powered up right into a snapshot or cumulative report supplies the visibility to deal with causes of non-productive time.
- Course of optimization: Log and sensor information can reveal patterns and correlations that allow high quality and productiveness enhancements. Optimizing drying occasions and coating thicknesses could be an instance.
Methods for Amassing Machine Knowledge
Probably the most profitable implementations are the place measurable enhancements are achieved rapidly. This may imply specializing in downtime discount, upkeep price financial savings, or bottleneck utilization enchancment.
Begin by deciding what information to gather, then think about methods to receive it. This implies establishing machine information assortment objectives earlier than selecting and putting in sensors and shopping for analytics software program.
Having recognized a goal, an audit will present what information is already out there. (It might be on PLCs, within the High quality Administration System [QMS], or within the ERP system.) This can even reveal gaps the place information is required however isn’t out there.
For information that’s at present unused, examine methods of getting it to a central location for storage and evaluation. This will imply including Ethernet connections to machines and organising communication strategies. Newer machines could have such skills built-in however older gear may have specialist units added.
The place information gaps exist, examine acceptable sensor applied sciences. Present draw, for instance, can point out whether or not a machine is working, idle, or not powered-up. For predictive upkeep extra temperature information, together with vibration and movement measurement, are sometimes key components in increase an image of machine well being.
Implement the sensors and the connectivity options chosen. Use analytics instruments to interpret the info, and search for patterns that present new insights into the explanations varied occasions or situations happen. Determine key metrics and arrange dashboards to make these seen.
Key Metrics to Observe
Collections of machine information haven’t any worth till they’re changed into actionable data. One strategy is to calculate and share or show metrics that permit folks see what’s occurring and the place they need to make enhancements.
A few of the critically necessary metrics are as follows.
Uptime and downtime
Understanding whether or not machines are working, or for a way most of the out there hours they’re working, is essential to discovering productiveness enhancements and figuring out upkeep effectiveness. That is particularly necessary within the bottleneck course of.
OEE
OEE (General Tools Effectiveness) is used to evaluate asset utilization and determine the place to focus enchancment efforts. Knowledge from PLCs and sensors like present screens can present among the numbers for the OEE calculation.
First move yield
This exhibits how a lot capability is being wasted by merchandise not assembly specs. It will probably typically be obtained from machine logs. Machine upkeep can typically cut back the variability that causes a low first-pass yield.
MTBF
MTBF (Imply Time Earlier than Failure) is an indicator of upkeep effectiveness. If predictive upkeep is being utilized successfully, machines will run for a very long time with out breaking down.
Vitality prices
Producers can measure the consumption of energy-intensive processes by putting in present or different sorts of meters. This may steer administration in the direction of the most important price discount targets.
Manufacturing quantity
Extra granular data on order standing improves visibility, enabling higher schedule adherence, larger machine utilization, and extra correct forecasting. Whereas the manufacturing execution system (MES) normally tracks orders, capturing information straight from the machines provides an instantaneous indication of manufacturing fee and yields information like cycle occasions that can be utilized to enhance manufacturing planning.
Trying Forward: Future Tendencies in Machine Knowledge Assortment
The variety of information assortment instruments out there to managers continues to develop whereas their prices fall and capabilities improve. IoT sensors are enabling far larger volumes and charges of information seize than has been the case earlier than.
This information will more and more feed right into a “digital thread” linking machines to the CMMS, elements inventories, and manufacturing schedules, all to cut back waste and enhance effectivity.
An rising problem is methods to take care of the amount of information that will likely be captured. A predictive upkeep system for instance could embody dozens of sensors put in on tons of of machines.
All through manufacturing, AI will develop into important. AI in predictive upkeep, for instance, will determine patterns and developments in machine well being, enabling it to advocate and schedule service and even order alternative elements.
The rising availability of machine information provides producers new methods to seek out and make operational enhancements. Elevated visibility and improved forecasting capabilities will result in issues being addressed on the first indication and earlier than they develop into pricey and disruptive.