Predictive Maintenance Solutions

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

Model Training & Deployment

Anomalies Detection

Fault Prognosis

Predictive maintenance and optimization

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

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

Airline Industry

Automotive Industry

Electric Power Sector

Industries We serve

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

Industries We serve

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

Industries We serve

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

Industries We serve

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

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.

Ford Figgs

USA

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.

Diego Rivera

Mexico

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.

Johan Walberg

Sweden

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.

Lasse Stefanoviski

Finland

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

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.

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.

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

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.

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.

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.

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