About
Predictive maintenance of systems ensures business continuity as it minimizes downtime of the critical components. Understanding and appreciating this importance, we have invested our time in development of AI based solutions and algorithms.We're excited to offer our solutions and expertise,which use Artificial Intelligence basedsolutions to monitor your equipment in real time. By leveraging the power of predictive analytics, we can identify potential issues before they become serious problems and lead to down time. Our solutions also help you to devise data intelligence based maintenance schedule and plans, hence minimizing unscheduled down-time and maintenance.
Our approach is extremely flexible and can be adjusted to your company's individual needs, taking into account aspects such as the type of equipment you use, the frequency of maintenance required, and the key performance indicators (KPIs) that are most critical to your operations.
To achieve these results, we use a range of data sources, including sensor data, telemetry data, and historical data,to build a comprehensive picture of your equipment's over time. This data is then fed into our machine learning algorithms, which use a variety of techniques, including regression analysis, decision trees, and neural networks, to identify patterns and predict when maintenance is needed.
Our Methodology
Data Collection: We gather relevant data from our clients' systems, such as sensors, machine logs, maintenance records, and other relevant sources, to collect the necessary data for predictive maintenance.
Data Preprocessing: Our expert data scientists clean, preprocess, and normalize the collected data to ensure consistency and accuracy. This includes removing outliers, filling in missing data, and converting data into a suitable format for analysis.
Feature Engineering: Our team identifies and extracts relevant features or variables from the preprocessed data that can be used to build predictive models. These features may include machine parameters, operational conditions, and maintenance history.
Model Development: Our experienced data scientists utilize advanced machine learning or statistical techniques to develop predictive models that can forecast machine failures or maintenance needs for our clients.This involves using algorithms such as regression, decision trees, or machine learning models like Random Forest, Support Vector or Deep Learning algorithms like LSTM (Long Short-Term Memory) for time-series data analysis.
Model Deployment: Once the models are trained and validated, we deploy them in our clients' production environment. This includes integrating the models into their IT infrastructure, creating APIs or microservices for real-time predictions, and setting up a monitoring system to track model performance.
Maintenance Alerts and Notifications: We develop a system to generate personalized alerts or notifications based on the predictions generated by the deployed models. These alerts are tailored to our clients' specific needs and can be used to notify their maintenance personnel or other relevant stakeholders about potential machine failures or needs in advance.
Our Implementation
Airlines: Our predictive maintenance solutions have been applied to aircraft engines, avionics systems, landing gears, and other aircraft components, enabling our airline clients to monitor health and predict maintenance needs. This has resulted in improved fleet reliability, reduced downtime, and enhanced operational efficiency.
Manufacturing: Our team has successfully implemented predictive maintenance solutions for industrial robots, CNC machines,conveyors, motors, pumps, and other production equipment used in the manufacturing industry. This has helped our manufacturing clients reduce downtime, increase production efficiency, and improve overall equipment reliability.
Spare Parts Management: We Implemented solutions for forecasting spare parts demand based on historical maintenance data and usage patterns, optimizing inventory and reducing costs associated with spare parts..
Alert and Notification System: Developing a system that generates alerts and notifications based on predictions from deployed models, providing timely information to clients about potential maintenance actions.
Oil and Gas: Our team has implemented predictive maintenance solutions for drilling rigs, pipelines, refineries, and other equipment used in the oil and gas industry. This has helped our clients in the oil and gas sector minimize downtime, optimize maintenance schedules, and improve overall equipment performance.
