Any values reported by or measured at a manufacturing machine are machine knowledge. This knowledge might describe what the machine is doing, particular course of variables, output knowledge, high quality knowledge, or knowledge concerning the surroundings inside the machine or the situation of the machine itself. Machine knowledge assortment additionally includes knowledge held on different techniques used to handle the plant.
Sorts of Machine Knowledge
Machines geared up with PLCs can acquire knowledge about processing situations or the work carried out. Machines will also be instrumented to assemble knowledge about their situation. There are a selection of alerts and data a machine knowledge assortment system can collect, reminiscent of the next.
Sensor knowledge
A number of the industrial machine sensors used for course of management can additionally seize machine well being knowledge. Examples are temperature, vibration, present draw, and oil situation. More and more, machine builders are utilizing “smart” sensors the place edge computing capabilities filter or course of knowledge earlier than transmitting it over a community.
Log knowledge
One of these knowledge quantifies what the machine has completed and is usually retained on a PLC or in PCs. It consists of machine manufacturing knowledge reminiscent of portions processed, cycle occasions achieved, yields or measurements, and different inspection outcomes. It could additionally cowl operational data reminiscent of run time and downtime.
Community knowledge
Some fashionable gear comes with IoT sensors put in that may ship knowledge to different techniques such because the CMMS. In different circumstances, producers retrofit machines with IoT units. Knowledge is distributed over wired Ethernet, Wi-Fi, Bluetooth, and even mobile networks when longer vary is required.
Many course of vegetation function SCADA (supervisory management and knowledge acquisition) techniques the place instrumented gear shares standing data with a central controller. This could possibly be course of parameters reminiscent of 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 knowledge offers visibility into what’s taking place within the manufacturing facility and might present insights into gear operation and efficiency. Makes use of embody the next:
- Predictive upkeep: Machine situation knowledge can drive a upkeep technique the place work is finished simply earlier than it turns into crucial. This reduces upkeep prices whereas additionally bettering machine availability.
- Yield enchancment: Course of knowledge reveals sources of variation that end in merchandise being outdoors of specs. This will drive enchancment efforts to search out and cut back waste.
- Growing utilization: Changing knowledge 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 knowledge can reveal patterns and correlations that allow high quality and productiveness enhancements. Optimizing drying occasions and coating thicknesses can be an instance.
Methods for Gathering Machine Knowledge
Probably the most profitable implementations are the place measurable enhancements are achieved rapidly. This will imply specializing in downtime discount, upkeep value financial savings, or bottleneck utilization enchancment.
Begin by deciding what knowledge to gather, then think about how you can receive it. This implies establishing machine knowledge assortment objectives earlier than selecting and putting in sensors and shopping for analytics software program.
Having recognized a goal, an audit will present what knowledge is already out there. (It might be on PLCs, within the High quality Administration System [QMS], or within the ERP system.) This will even reveal gaps the place knowledge is required however shouldn’t be out there.
For knowledge that’s presently 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 talents built-in however older gear might have specialist units added.
The place knowledge 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 knowledge, together with vibration and circulate 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 numerous occasions or situations happen. Establish key metrics and arrange dashboards to make these seen.
Key Metrics to Observe
Collections of machine knowledge don’t have any worth till they’re become actionable data. One strategy is to calculate and share or show metrics that allow folks see what’s taking place and the place they need to make enhancements.
A number of the critically necessary metrics are as follows.
Uptime and downtime
Realizing whether or not machines are working, or for the way most of the out there hours they’re working, is vital to discovering productiveness enhancements and figuring out upkeep effectiveness. That is particularly necessary within the bottleneck course of.
OEE
OEE (Total Gear Effectiveness) is used to evaluate asset utilization and determine the place to focus enchancment efforts. Knowledge from PLCs and sensors like present displays can present among the numbers for the OEE calculation.
First cross yield
This exhibits how a lot capability is being wasted by merchandise not assembly specs. It could 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 forms of meters. It will steer administration in the direction of the most important value discount targets.
Manufacturing quantity
Extra granular data on order standing improves visibility, enabling higher schedule adherence, increased machine utilization, and extra correct forecasting. Whereas the manufacturing execution system (MES) often tracks orders, capturing knowledge instantly from the machines offers an instantaneous indication of manufacturing price and yields knowledge like cycle occasions that can be utilized to enhance manufacturing planning.
Wanting Forward: Future Traits in Machine Knowledge Assortment
The variety of knowledge assortment instruments out there to managers continues to develop whereas their prices fall and capabilities enhance. IoT sensors are enabling far better volumes and charges of knowledge seize than has been the case earlier than.
This knowledge will more and more feed right into a “digital thread” linking machines to the CMMS, components inventories, and manufacturing schedules, all to scale back waste and enhance effectivity.
An rising problem is how you can cope with the amount of knowledge that can be captured. A predictive upkeep system for instance could embody dozens of sensors put in on lots of of machines.
All through manufacturing, AI will develop into important. AI in predictive upkeep, for instance, will determine patterns and tendencies in machine well being, enabling it to advocate and schedule service and even order substitute components.
The rising availability of machine knowledge offers producers new methods to search 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 expensive and disruptive.