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Are you ready to predict the future of your business? Would you like to leverage the power of predictive analytics to stay ahead of the curve?
Predictive maintenance is an emerging concept within Industry 4.0 that enables industries to use data-rich algorithms to anticipate when an asset or equipment will be due for maintenance. This real-time analysis technology, that studies historical data patterns, offers an array of promising solutions including achieving cost savings goals and allowing companies to take time-based preventive measures.
Common industry challenges
Are you struggling to manage your assets on time?
Do you want to maintain spare part inventory?
Are you worried about the life of your equipment?
Can you manage your equipment remotely?
Can you ensure the safety of operators?
Benefits of Predictive Maintenance
Reduced Repair Costs
Prolonged Machine Life
Validation of Repairs
How Synctactic AI supports Predictive Maintenance?
SynctacticAI’s Sync Learn offering enables interpretation of sensor data that adheres to improving revenue and reducing cost. Powered with AI and ML algorithms, predictive maintenance is the technique of using in-memory data to improve revenue and reduce cost by making timely repair decisions. Together they help companies to leverage predictive maintenance in vivid ways.
Life Cycle Estimation
By targeting the equipment life cycle variable, a model can be generated into the production environment to answer the questions regarding the lifespan of the equipment.
Open Maintenance Windows
SynctacticAI lets you train your business model using a variety of libraries. Using the training and evolution model, you can take timely actions of maintaining your equipment before any real damage occurs.
Any faults present in the machinery can be easily pointed out by the sync process of data discovery and analysis. When the fault is detected beforehand, a vast damage can be easily prevented.
When data is processed on a preset scale, then the prediction failures are easy to locate and instant alerts are automatically generated to notify the users.