Predictive Maintenances

Smart O&M

AI Predicts First, Smarter Equipment Maintenance

24/7 Continuous

Monitoring

Plug-and-Play

System

Automated Analysis

and Alerts

Why is Smart O&M Needed?

Smart O&M is vital for industries relying on large motors and critical process equipment—such as semiconductors, manufacturing, textiles, and petrochemicals—where failures risk costly downtime and production losses.

Common challenges across these industries include

Inability to tolerate unplanned downtime

Core motor equipment drives entire production lines, and sudden failures can cause full-scale shutdowns.

Hard-to-detect warning signs

Traditional maintenance depends on scheduled inspections or operator experience, making it easy to miss early signals of failure.

High repair and downtime costs

Emergency repairs often require substantial time and expense, with potential impacts on delivery schedules and customer relationships.

Lack of real-time predictive capability

Standard monitoring shows only current equipment status, without forecasting potential failures in advance.

Solution

This solution combines AI modules and predictive maintenance software to detect early equipment issues, enabling proactive maintenance and reducing downtime and repair costs.

High-Frequency Current Collection

AI Health Assessment Module

Maintenance Work Order Management

How it works

01

Comprehensive Requirement Analysis

Perform an in-depth site survey to understand operational conditions and constraints
Collaborate with stakeholders to define clear customer expectations and objectives
Establish baseline parameters for equipment deployment and performance metrics
Define project scope, boundaries, and detailed deliverables

02

Efficient Baseline Data Acquisition

Precise and professional setup by expert field engineering teams
Implement minimally invasive deployment to avoid operational disruptions
Enable rapid deployment to begin collecting valuable data immediately
Ensure robust data quality through standardized acquisition protocols

03

Advanced Data Analysis and Model Optimization

Utilize proprietary signal processing techniques and convolutional neural network (CNN) models for accurate classification
Optionally supplement with classical spectral analysis-based fault detection
Integrate equipment-specific characteristics to calculate a comprehensive health score
Continuously refine models using real-time data for enhanced predictive accuracy

04

Residual Useful Life (RUL) Estimation and Maintenance Planning

Conduct trend analysis of equipment health scores to identify degradation patterns
Leverage historical and industry-standard equipment lifecycle data for context
Develop equipment lifecycle profiles based on customer-specific usage scenarios and environment
Optimize RUL estimates and provide proactive maintenance recommendations