Predictive Maintenance & Condition Monitoring Solutions

Combine machine data, sensors, operating context, trends, alerts, and analytics to support condition-based and predictive maintenance decisions.

ConditionMonitoring

AnomalyTrend Detection

MaintenanceDecision Support

Predictive maintenance condition monitoring dashboard

Move from Calendar-Only Maintenance to Condition-Informed Decisions

Unexpected failures affect production, quality, delivery, and maintenance workload, while servicing too early can create unnecessary cost.

MayilVeera develops condition-monitoring and predictive-maintenance solutions that collect relevant equipment and process information, establish baselines, identify changes, and present evidence to maintenance teams.

Predictive maintenance is a decision-support approach. It does not guarantee every failure will be predicted and must be validated for each asset and failure mode.

Solution Capabilities

Projects can begin with rules and trends before moving to statistical or machine-learning models where data quality supports it.

Critical-Asset Assessment

Identify assets with useful monitoring value by reviewing criticality, failure modes, consequences, instrumentation, history, and required warning time.

Sensor and Machine Data Integration

Collect vibration, temperature, current, pressure, flow, speed, runtime, PLC, drive, alarm, oil, observation, and maintenance data.

Baseline and Condition Trending

Track normal ranges, load-dependent behavior, temperature, vibration, current, runtime, alarms, start-up, and period-to-period changes.

Rules and Threshold Alerts

Support maintainable alerts for temperature, vibration, current imbalance, runtime, overload, flow, pressure, cycle time, and communication loss.

Anomaly and Predictive Models

Evaluate multivariable anomaly detection, operating-state classification, degradation indicators, and failure-risk scoring where sufficient data exists.

Maintenance Workflow Support

Help teams review evidence, classify alerts, plan inspections, record outcomes, and improve rules or models over time.

Core Reliability Capabilities

The selected signals must relate to the targeted failure mode, otherwise the model cannot provide meaningful warning.

Condition trends

Threshold alerts

Anomaly detection

Maintenance history

PLC and sensor data

Asset assessment

Evidence review

Edge/local/cloud analytics

How a Maintenance Pilot Works

Start with one meaningful asset and validate alert usefulness before scaling.

Asset Selection

Choose an asset with high consequence, accessible signals, and known failure modes.

Data Plan

Select sensors, PLC data, sampling rates, context, and storage architecture.

Baseline

Capture normal operating ranges and load-dependent behavior.

Alert Rules

Configure practical alerts and escalation rules for maintainers.

Analytics

Evaluate anomaly or predictive methods when enough representative data exists.

Validation

Compare alerts against inspections, maintenance events, and operator feedback.

Maintenance Outputs

The value comes from useful evidence, not from claiming prediction where the data cannot support it.

01Critical asset list

02Failure mode mapping

03Sensor and PLC signal plan

04Baseline dashboard

05Condition trend views

06Alert and escalation rules

07Maintenance evidence logs

08Pilot validation report

09Improvement recommendations

10Scale-up roadmap

Frequently Asked Questions

Can predictive maintenance prevent all failures?

No. It supports earlier detection and better decisions, but each asset and failure mode must be validated.

Do we need sensors?

Often yes, but useful data may also come from PLCs, drives, alarms, runtime, cycle counts, and maintenance history.

Can we start without machine learning?

Yes. Rules, baselines, and trend monitoring are often the right first stage.

How do we choose the first asset?

Start with a critical asset that has known failure modes, available signals, and measurable operational impact.

Start with a Critical Asset and Real Failure Mode

Share your asset type, failure concerns, available sensor or PLC data, maintenance history, and expected warning time.

Discuss a Maintenance Pilot