We depend on a variety of machines in our everyday lives—unless they are maintained properly, these machines eventually fail.
Since the machinery and its functions play a crucial role in the plant’s operation, restoring the damaged parts after failing would be highly expensive.
This ultimately affects plant productivity, hence we need solutions that prevent this condition and improve plant safety.
This blog throws light on AI based predictive maintenance for machine efficiency and the benefits it serves to your business.
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Why choose Predictive Maintenance over Preventive maintenance?
The plant owners go for various maintenance techniques to enhance operational reliability and minimize expenses.
The most common methods used in the industries are preventive maintenance and predictive maintenance.
The preventive maintenance approach emphasizes making efforts to schedule maintenance programs in advance of a possible failure, focusing on avoiding potential machine defects.
However, scheduling it too early is expensive, and can put the machine’s life at stake since you go for maintenance while it is still usable.
On the other hand, foreseeing when a machine might break down helps you schedule its maintenance beforehand.
This process is called Predictive maintenance, which outperforms all the other methods since you can cut down the downtime and boost output with the method.
It locates complex machinery-related problems and helps you determine which components require repair before the machine could go into serious damage.
How is AI Based predictive maintenance implemented?
Predictive maintenance is a kind of condition-based maintenance approach where the sensor devices monitor the machines, and provide information on the asset’s condition.
This data describes whenever the assets require servicing to prevent equipment failure.
Filtering through massive amounts of data to segregate the actionable and relevant data is one of the most crucial aspects of predictive maintenance.
With the advent of technologies, organizations can find themselves data-rich but poor in terms of adequate information.
AI-based predictive maintenance systems help to extract insights that further spot developing defects before they turn into severe problems.
It also lets you analyze the remaining durability of the assets with issues, plan the repairs in case of a disruptive ecosystem, and consider a root-cause study to avoid future failures.
AI Based Predictive Maintenance Workflow
Based on the needs and specifications of the machine, the predictive maintenance works with the following steps:
1. Data Acquisition
The development of cloud technologies and IoT play a crucial role in predictive maintenance. IoT sensors are responsible for collecting all relevant data and storing it in the cloud throughout the entire lifecycle of a machine.
This can range from the weather conditions, data usage of equipment, and the manual data after human inspection.
2. Data Analysis
Once the facility managers or the technicians earn data relevant to the functioning of the assets, now it’s important to analyze the data.
With AI, you don’t need to be a trained data scientist. Predictive maintenance utilizes many significant AI tools to let the technicians or managers make the best evaluation of the state of the machine.
3. Health evaluation of assets
AI tools are utilized to implement data analysis and perform evaluations of the health of the assets. When a need arises, AI creates recommendations on the actions needed to maintain the sustained growth of the asset.
These recommendations are usually the alerts that remind technicians about the actions they should implement. It could be an invitation for a simple software update or even complex repair alerts.
Based on the systems in use, the notifications for predictive maintenance can be on a visual display, reminders sent to a mobile application, or a feedback method to grab the attention of the system manager.
In this way, the facility personnel or technicians can implement adequate updates and repairs to manage the machine’s health while also taking care of its efficiency and safety.
In this method, machine learning models can engage in data collection and analysis from various sources to identify the conditions that might cause failure and build precise predictions on the time to take preventive measures.
How does predictive maintenance make things better with AI?
- Minimized maintenance costs
Businesses can save on costs and time with the ability to identify and prevent breakdowns of equipment. Improved maintenance planning cuts down your expenses, particularly in asset-specific industries.
Since the AI-driven predictive maintenance systems utilize historical data from various sources such as IoT, sensors, etc. they generate accurate forecasts regarding the machine’s health, risk of failure, usage, etc. which lets you take actions depending on these insights.
- More production time
The predictive maintenance system helps to understand both internal and external sources of delays. This establishes techniques to resolve the issues and find any technical problems before they turn irreversible.
You can receive warning signals, and alerts to increase the uptime of assets, and boost efficiency and dependability.
- Long machine life
Real-time monitoring helps you to gain an in-depth understanding of your machines, to let you predict possible machine failure, and locate the parts to replace.
You can also schedule repairs and maintenance, get real-time notifications, and respond quickly to ensure the long life of the assets.
- Great working environment
The maintenance team can detect the health of the field assets frequently through AI-driven machine monitoring techniques.
Unplanned downtime and machine failure need relocation of the service crew to other locations, getting more personnel to work, or entirely rearranging the maintenance tasks to manage the issue. AI-based predictive maintenance prevents this and reduces the maintenance length.
- Scaling up the overall safety and compliance
Maintenance engineers can identify and manage potential safety issues and difficulties with predictive maintenance before they impact workers.
Through data assessment from different internal and external sources, you can get swift necessary actions to avoid safety hazards.
In a Nutshell
Unplanned plant downtime is a major concern that affects plant productivity. Unexpected machine failure results in a destructive impact on the overall process.
Whereas modern AI-based predictive solutions help you carry out timely maintenance, and reduce plant failures, labor costs, spare parts use, loss of production etc.
It improves the overall throughput of the system and lets you prevent disruptions to enjoy flawless machine operations.
At Ocean, we offer the best predictive maintenance services to help companies track their machine, and equipment, predict future equipment failures and make the best decisions for proactive AI-driven maintenance.