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Key Concepts Involved
The entire predictive maintenance can be categorized into five crucial components. The synergy of these components streamlines the operation flow for improved Machine health.
Data Collection & Preprocessing
In this phase, we leverage some of the robust tools to collect Data from two primary sources. We train our ML models from sensor-based log integration in the equipment and the Database server with Historical data. This data is filtered through multiple parameters like temperature, and pressure controls, We clean and format the sensor data for better Data usability.
- Install sensors into equipment
- Data collection and storage for analysis
- Data Integration into Our ML Model
Model Training & Deployment
We integrate vast amounts of data into our model for extensive training. The Data sources include sensor logs, Equipment data, and external databases. We train the ML models rigorously which allows businesses to take proactive measures to prevent equipment failures.
- Real-time insights
- Optimize operational Flow
- Maximize the Productivity
Anomalies Detection
Trained from various sources, our ML model identifies different hidden patterns or machinery defects that are difficult to comprehend manually. By identifying underlying complex patterns, anomalies can be detected. This can benefit several industries including the manufacturing industry and supply chain industry in the following ways.
- Reduces Unscheduled Downtime
- Boosts equipment efficiency.
- Increased Revenue and Profits.
Fault Prognosis
Once an anomaly is detected, the root cause is investigated. Considering the anomaly severity, proactive actions are taken to eliminate it in the future. Thus predictive maintenance shows its positive impact on your enterprise’s scaling attribute.
- Root cause Analysis
- Predictive modeling
- Scheduling tasks on a priority basis
Predictive maintenance and optimization
The predictive maintenance and optimization revolve around streamlining the entire operational flow. Based on the priority, maintenance tasks are identified and scheduled. This can significantly eliminate the possibility of undesirable situations to occur in the future.
- Real-time Performance Monitoring
- System Evaluation
- Maintenance scheduling
Innovative Features of Our Predictive Maintenance Solutions
We leverage robust features that can analyze vast amounts of information to boost product efficiency and improve overall product quality. Our PdM solution predicts foreseen future events that can be prevented with real-time informed actions.
Advanced machine learning algorithms IoT-based Predictive maintenance Alerts Artificial Intelligence integration Advanced Analytics Machine Learning Model Development Real-time System Monitoring Our Predictive Maintenance Solutions in Action - With Industry cases
Our machine predictive maintenance use- cases have empowered industries of all verticals by taking some business-driven decisions in real-time. Enterprises can proactively leverage our predictive machine learning models for managing their assets and establishing automation in the entire process.
Manufacturing Industry
Predictive maintenance solutions in the manufacturing industry help the Operator take immediate actions in case of system failures. Preventive measures can reduce unprecedented downtime.
- Timely Machine maintenance
- Reduced unscheduled downtime
- Improved asset management
- Optimized operational flow
Airline Industry
Predictive maintenance systems are a non-negotiable approach in the airline industry. It allows for tracking the Airplane's performance and detecting any system failures. It allows to take proactive measures against any damages in several parts of the airplane allowing timely maintenance for its smooth functioning.
- Early detection of issues
- Smooth functioning of hardware parts
- Micro-management of the Hardware parts
- Proactive measures in case of system failures
Automotive Industry
Due to the rapid advances in the Automotive industry, predictive maintenance software can surpass from reactive repairing to proactive timely maintenance. Equipped with sensor-based systems like cameras, a vast amount of Data can be worked upon to enable preventive measures. IoT-enabled predictive software focuses on optimizing machinery health to boost its long-term efficiency.
- Optimizing the Robot Performance
- Reduce machinery wear and tear
- Maintaining Paint shop equipment
- Reduced Delay for pressing and welding equipment
Electric Power Sector
Electric power has to adhere to mandatory guidelines to maintain the power supply 24/7. In such situations, power manufacturers can leverage predictive maintenance solutions to increase the robustness of the process by identifying defects if they exist in the overall system. This will ensure the quality monitoring and smooth functioning of the components of the turbine flow.
- Quality Monitoring of the process
- Monitoring hydraulic valves and lubricants
- Maintaining the tightening process
- Vibration Analysis of Milling
Harness the Power of your data to make informed decisions
Transform your Manufacturing
Operations
Make Futuristic Move What our clients say !
Our company deals with critical infrastructure that require precise maintenance hence we hired Yudiz for the following and I can say I am pretty satisfied with the results.
Yudiz has a very agile and collaborative way to achieve the best solutions. They were able to deeply understand our demands and what and the stakes and they delivered.
Integrating our system with an AI predictive maintenance solution had become a necessity and Yudiz made sure to execute their development strategy to fulfill it in a cost-effective manner.
The best part was their predictive maintenance solutions have a guiding and user-friendly design. Which helped our workers and staff to quickly get used to the solutions.
Success Snapshot
With world-class design for a fraction of the cost of hiring a team,it's a no-brainer, right?
FactoryHealth AI
FactoryHealth AI leverages predictive maintenance technologies to extend the lifespan of machinery, minimize unplanned downtime, and bolster production efficiency in heavy manufacturing.
- Equipment Failure Prediction
- Vibration Analysis
- Thermal Imaging Insights
- Production Line Optimization
- Cost Management
MaintainMax AI
MaintainMax AI utilizes advanced predictive analytics to enhance maintenance strategies, ensuring operational continuity and safety in the oil and gas sector.
- Engine Health Monitoring
- Component Wear Prediction
- Maintenance Crew Dispatching
- Safety System Checks
- Downtime Reduction
Frequently asked questions
1. What are Predictive maintenance solutions?
A predictive maintenance solution collects large numbers of data from IOT-enabled systems and analyzes the data thoroughly to identify any potential threat to the smooth functioning of the equipment systems.
The biggest advantage is that the predictive model rigorously monitors the system with any kind of interference and identifies any abnormalities in the form of equipment failure. This significantly reduces the system downtime which is the major cause of high operational expenses.
2. How IoT and AI are used in Predictive maintenance?
Sensor-based IoT systems are enabled in the devices that aggregate large amounts of data. The data can be tracked constantly to monitor the status of the equipment without any physical inspection.
Artificial intelligence solutions can analyze vast amounts and extract meaningful insights about the system. For example, AI can identify any deviation from the normal functioning of the system. The machine learning algorithms are trained on historical data and real-time sensor-based data to learn how the system normally functions and operates.
3. What are the types of Predictive maintenance?
There are mainly three types of predictive maintenance that includes:-
- Preventive maintenance – These systems constantly keep an eye on the system by monitoring real-data censored-based machine data. Any anomaly detection can be identified with effective proactive measures.
- Condition-based – Such systems are highly dependent upon the sensor-generated data. It successfully identifies the performance of teh machines. In scenarios where the machine’s performance has degraded, it takes accurate preventive measures to improve the machine’s health and improve overall performance.
- Risk-based – These systems only prioritize those equipment that are most likely prone to failure. It recommends the most cost-effective solution to reduce equipment downtime
4. How can predictive maintenance impact the overall business performance?
Predictive maintenance can reduce the downtime, improving the operational efficiency and machine health. This helps businesses by reducing operational costs and preventing undesirable situations. Businesses can leverage PDM solutions and gain a competitive edge in the market.
5. How can I integrate predictive maintenance solutions into your system or workflows?
Yes, you can successfully integrate Predictive maintenance solutions into your business by adding sensors to your machines and software that collects all your system data. To integrate predictive models, you need a prominent Predictive maintenance company comprising of an IT team and operational experts.
6. What is the ongoing cost of implementing Predictive maintenance solutions?
The two major costs associated with implementing Predictive maintenance solutions include data management and software updates. While the production setup can be highly expensive its long-term benefits like reduced operational costs can outweigh the traditional maintenance.
