Our Solution

Forecast machine future health

Prognosis is centered on predicting the future health status of machines. The damage in machines takes some time to build and even if premature faults have developed, the machine has some useful life left before the final failure occurs. This time-period provides a measure of the remaining useful life of the machines. i-MoniTech provides intelligent solutions based on machine learning and deep learning algorithms to forecast the machine future health condition and calculate the remaining useful life enabling effective planning and scheduling of the maintenance actions.

Are you facing these challenges?

Unexpected Machine Failures

Concerned about sudden machine breakdowns disrupting your operations and leading to significant financial losses?

Inefficient Maintenance Scheduling

Struggling with planning and scheduling maintenance activities effectively, resulting in either over-maintenance or missed critical repairs?

Limited Insight into Machine Health

Lacking accurate predictions about the future health status of your machines, making it difficult to make informed decisions about maintenance and operations?

High Operational Costs

Facing increased operational costs due to unplanned downtime, emergency repairs, and inefficient use of resources?

Key Features

Predictive Analytics

Leverage advanced machine learning algorithms to accurately predict the future health status of your machines.

Remaining Useful Life Calculation

Determine the remaining useful life (RUL) of your machinery, enabling proactive maintenance planning and resource allocation.

Early Fault Detection

Identify and assess premature faults, providing critical insights into potential failures well before they occur.

Optimized Maintenance Scheduling

Create efficient maintenance schedules based on predictive data, reducing downtime and extending the lifespan of your equipment.

Intelligent Reporting

Receive comprehensive reports that include detailed prognostic data and actionable recommendations for maintenance actions.

Enhanced Operational Efficiency

Improve overall operational efficiency by minimizing unexpected breakdowns, reducing maintenance costs, and maximizing machine uptime.

The way we conduct our activities

1. Book service / onsite visit/ remote services

Client can book our services through phone call/email and our expert team visits the site to collect diagnosis data from critical machinery installed on the factory shop floor. Client can also share their machine data remotely if it is already available. Monitored machine include pumps, motors, gearboxes, turbines, compressors, engines and fans, etc.

2. Collect data via IOT sensors and devices

We install high-end IOT sensors and devices on the machine to collect vibration /acoustic emission/ temperature data to capture the machine health status. Data is stored and transferred to a PC for further analysis. We document the preliminary information about the machine operating conditions, past operation profile, process parameters fault symptoms, and other relevant technical details available with the client .

3. Signal processing and feature extraction

We offer innovative data analytics tools with a range of signal processing algorithms to discover superior fault features that can track the machine condition accurately and useful to build robust AI models. Features are extracted to reveal the time-domain, frequency-domain and time-frequency domain information embedded in signals.

4. Detecting faults through through AI

We use state of the art machine learning and deep learning algorithms to learn the fault patterns directly from the raw signals or through the fault features for identifying and classifying machine faults. Our solutions are designed to tackle challenges such as unavailability of failure data in training, missing data, imbalanced data and explainability

5. Visualization and Interpretation

Our software platform loaded with various data visualization and interpretation dashboards/icons enables the users to visualize the health indicators and forecast them in a real-time manner, set failure thresholds, get insights into analysis outcomes, and generate accurate failure alarms.