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Unplanned downtime is a silent killer of profits, and it bleeds money, whereas conventional preventive maintenance is an ineffective waste of resources in repairing what is not broken. Such a legacy model puts teams under high stress, reactive loopholes that interrupt supply chains and escalate inventory expenses.
We are currently transitioning from industry 4.0 data-driven foresight of the decisions that are based on intuition to the ones that are maximized through the process of total assets reliability. This is not just mere maintenance but instead takes advantage of real-time knowledge in responding at the specific time when it is needed, as opposed to the calendar.
But useless data is data without action, and here is where Cryotos comes in as the medium of transformation to streamline processes out of raw numbers. It can simplify complex analytics into practicable tasks so that technicians can, in reality, exploit big data to prevent downtimes.
To gain control of predictive maintenance (PdM), we must initially remove the buzzwords and examine the underpinning.
It isn't just "a lot of spreadsheets." In an industrial context, big data is defined by the 3 Vs:
The contrast is very basic and significant.
Preventive Maintenance is Time-based (e.g. Change oil after every three months.) This is ideal in avoiding failure, but most of the time results in over-maintenance.
Predictive Maintenance is Condition-based. (e.g., "Replace oil when it has become viscous less than X.) This is the best place of intervention- correcting the asset just when it is required and maximizing its life span.
Big data enables us to perform things which are beyond human capability.
Moving to a big data approach doesn't happen overnight. It requires a structured implementation.
Do not try to monitor everything. Putting vibration sensors on the breakroom exhaust fan is a waste of resources. Focus on the top 20% of assets that cause 80% of your downtime and maintenance costs.
Select the appropriate input to the failure mode that you are attempting to capture.
You have to teach the system what "Normal" looks like. Big data requires a baseline period where the algorithms learn the standard operating temperature or vibration frequency of a machine, so it can flag deviations later.
This is the most critical step. A red light on a dashboard does not fix a machine; a technician does. You need to integrate your data stream with a system like Cryotos. When a data spike occurs, then CMMS should automatically trigger a work order, assign it to the right technician, and attach the relevant fault data.
After the repair, the technician closes the work order. This data must go back into the model to validate the prediction. Did the machine actually have a fault? This makes the algorithm smarter over time.
The central nervous system of this data is designed to be Cryotos. It does the heavy work of business value translation of the bytes.
Three technologies have to operate together in order to utilize big data fully. Consider it as a biological system:
Real-time physical data is gathered by the Internet of Things. The smart sensors are nerve endings that sense vibration, heat, and sound and transport that reality to the cloud.
Machine Learning (ML) is a process that examines the history of massive data to interpret patterns. It uses specific algorithms:
AI takes us out of analysis and action. In a Prescriptive Maintenance model, AI does not simply inform you that the machine is faulty but suggests the fix. As an example, Cryotos applies Generative AI to assist in building work orders and recommending solutions, using past repair data.
Big Data is not only a technological strategy of big companies, but also the future of sustainable, cost-effective maintenance. Moving away instead of reactive firefighting, to data-driven prediction, not only do you extend the life of your assets, but you also improve the level of safety and ultimately, your bottom line may increase significantly.
Your machine already has the data. The system to interpret it is the only lacking element.
Willing to transform your information to prevent downtime? Learn how Cryotos could use the power of big data to mechanize your preventive maintenance and protect your prime assets.
