Predictive Maintenance

Transform your business environment with
Predictive Maintenance

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?
If you are facing a problem of being unable to repair your plant equipment on time which eventually leads to disturbance in supply chain models, then predictive maintenance is your solution here. This technology will show you when a piece of certain equipment needs maintenance so that you can quickly get the repair done without affecting your business model.
Do you want to maintain spare part inventory?
It is the biggest challenge for the IoT hubs to have an adequate supply of spare parts as some mechanical parts need to be preordered. But, by using predictive maintenance when detailed information about defective parts is available, then businesses can easily stock up the inventory of spare parts.
Are you worried about the life of your equipment?
With adequate information, you can prevent catastrophic failures and detect problems in the machine at an early stage. Thus, you can take preventive measures quicker and increase the lifespan of your machine.
Can you manage your equipment remotely?
The remote management of the equipment can be easily processed with proper predictive data. With detailed equipment reports, management can remotely make decisions regarding the repair or safety of the machinery.
Can you ensure the safety of operators?
Early warnings of system problems or failures can reduce the level of risk involved for operators. The personal injuries and number of deaths due to machine failure can be reduced, which will eventually lead to insurance benefits to the organizations.

Benefits of Predictive Maintenance

01.
Reduced Repair Costs
The costs related to maintenance and repair can be significantly reduced by adopting predictive maintenance. By predicting repairs in advance, costs involved in labor, tools, replacement and other maintenance overheads can be brought down.
02.
Lesser Downtime
With the precise knowledge of equipment, the maintenance team can take swift actions and repair the machines beforehand. This helps in greatly reducing downtime of the manufacturing units without impacting productivity.
03.
Prolonged Machine Life
Some damages can be easily controlled with timely actions. Thus, with detailed plant equipment reports, the average life of equipment can be increased because the actual health of the machine is improved in time.
04.
Improved Production
The availability of process systems increases after the implementation of a condition-based predictive maintenance program. Thus, complete proactive maintenance including process parameter monitoring can enhance the operating efficiency multiple notches up.
05.
Validation of Repairs
Sync analysis can be utilized to determine whether a repair done on the machinery has eliminated the problem or has led to an additional problem. Predictive maintenance evaluates the status of a repair and reports an incompetent repair.
06.
Elevated Profits
Predictive Maintenance positively influences the overall manufacturing operations and processing plants. The real-time processing of plant benefits in offsetting the cost of the equipment needed for implementation of the processes. On the top, data driven predictive maintenance can further reduce annual spending costs.
07.
Enhanced Safety
With the timely information about damaged machinery, the additional safety can be offered to the machine operators as one small failure in the machinery can lead to injury or death. Additionally, a safety nest can even uplift the morale and productivity of the operators.
08.
Inventory Management
Some machine parts need to be pre-ordered or manufactured on special demand only. Thus, with the correct maintenance data, you can keep the stock of the critical spare parts that need immediate attention.

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.

Fault Classification

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.

Warning Alerts

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.

Would you like to implement a Predictive Maintenance Program with SynctacticAI? Our Sync Learn offering enables companies to draw meaningful outcomes from the massive pools of data to transform business efficiency.

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