Glossary of Condition Monitoring: Observation of Equipment Condition in Maintenance

Condition monitoring is at the forefront of modern maintenance and reliability engineering. This glossary provides in-depth definitions and practical context for key terms and technologies, drawing from ISO 17359, ICAO standards, and global best practices across industries.

Acoustic Analysis / Acoustic Monitoring

Definition:
Acoustic analysis is a condition monitoring technique that captures and interprets sound waves—both audible and ultrasonic—to assess the operational health of machinery. It utilizes sensors such as microphones and piezoelectric transducers to detect anomalies in noise patterns, which can indicate issues such as bearing wear, leaks, or electrical faults.

Applications:

• Audible Monitoring: Detects abnormal sounds (grinding, squealing) linked to mechanical wear.
• Ultrasonic Monitoring: Identifies high-frequency emissions from compressed air leaks, electrical arcing, or cavitation in pumps, often before issues become audible.

Industry Example:
In aviation, ultrasonic detectors are used to pinpoint fuel system leaks or early material cracks, enhancing safety and reliability.

Asset Criticality

Definition:
Asset criticality is the process of evaluating how essential an asset is to operations, safety, and business objectives. This assessment determines the level of monitoring and maintenance resources allocated to each asset.

Determination Factors:

• Function in process
• Consequence of failure (safety, environment, production)
• Redundancy and repair time
• Spare part availability

Best Practice:
High-criticality assets (e.g., main compressors, aircraft engines) warrant continuous, advanced monitoring. Lower-criticality assets may rely on periodic checks.

Baseline Values

Definition:
Baseline values are reference measurements recorded during ideal operating conditions, serving as the standard for detecting deviations.

How to Establish:

• During commissioning or post-overhaul
• Under stable, controlled conditions
• Capture parameters like vibration, temperature, pressure, and oil quality

Importance:
Comparing current data to baselines allows early detection of wear or faults and supports machine learning algorithms in predictive maintenance.

Condition-Based Maintenance (CbM)

Definition:
CbM is a maintenance strategy that triggers interventions based on actual asset health, as indicated by real-time or periodic monitoring, rather than fixed schedules.

Components:

• Sensors (vibration, temperature, oil, etc.)
• Analytics platforms
• Thresholds for alerting

Advantages:
Reduces unnecessary maintenance, cuts costs, and prevents unexpected failures—endorsed by ICAO and ISO 17359 for critical assets.

Example:
Aircraft components are replaced when data indicates wear, not merely at flight-hour intervals.

Condition Monitoring

Definition:
Condition monitoring is the ongoing process of capturing and analyzing key indicators (vibration, temperature, pressure, lubrication, acoustics) of machinery health to prevent failures and optimize performance.

Parameters & Technologies:

• Vibration, temperature, electrical current, oil quality, acoustic emissions
• Sensors, DAQ systems, analytics platforms

Compliance:
Guided by international standards such as ISO 17359 and ICAO maintenance protocols.

Example:
Jet engines are monitored for vibration anomalies to prevent blade failures.

Condition Monitoring Software

Definition:
A dedicated digital platform that aggregates, visualizes, and analyzes sensor data from industrial assets, enabling real-time diagnostics, alerts, and reporting.

Features:

• Data integration from multiple sensor types
• Real-time visualization dashboards
• Automated alerts and work orders
• Advanced analytics and AI
• Integration with EAM and CMMS systems

Use Case:
Centralized monitoring of thousands of assets, triggering maintenance only when warranted by real data.

Continuous Monitoring

Definition:
A method of real-time, uninterrupted tracking of equipment condition using permanently installed sensors. Essential for high-risk or critical assets.

Benefits:

• Instant detection of faults
• Eliminates missed transient issues
• Required in industries like aviation, nuclear power, and oil & gas

Example:
Wind turbine gearboxes are monitored continuously to detect bearing failures before breakdown.

Data Acquisition (DAQ) System

Definition:
DAQ systems are hardware/software solutions that collect, digitize, and transmit sensor data for condition monitoring.

Components:

• Sensor interfaces (analog/digital)
• Signal conditioning, A/D conversion
• Communication (Ethernet, wireless, serial)

Scalability:
From simple multi-channel loggers to enterprise systems integrating with SCADA, DCS, and cloud analytics.

Detection, Diagnosis, Prognosis, and Programme (The Four Pillars)

Framework:

1. Detection: Early identification of abnormal conditions via sensors/analytics
2. Diagnosis: Determining root cause (manual, algorithmic, AI)
3. Prognosis: Predicting time to failure and intervention window
4. Programme: Planning and executing maintenance actions

Value:
Optimizes maintenance, reduces downtime, and supports continuous improvement.

Electrical Monitoring

Definition:
Monitoring of electrical parameters (voltage, current, resistance, power factor) to assess equipment health and prevent failures.

Key Techniques:

• Current signature analysis for motors
• Insulation resistance for windings/cables
• Power quality for voltage/harmonics

Examples:
Detecting insulation breakdown in generator windings, preventing catastrophic short circuits.

Electromagnetic Measurement

Definition:
Non-destructive testing methods using magnetic fields (eddy current, magnetic flux leakage) to detect defects in metal components.

Techniques:

• Eddy current detects cracks/corrosion in conductive materials
• Magnetic flux leakage highlights pitting and cracks in ferromagnetic structures

Industry Use:
Critical in pipelines, storage tanks, aircraft, and railways.

Failure Mode

Definition:
A specific way in which an asset or component can fail. Classification of failure modes informs monitoring and maintenance strategies.

Types:

• Mechanical (bearing wear, misalignment)
• Electrical (insulation breakdown)
• Thermal (overheating)

Standards:
FMEA and ICAO MSG-3 are used for systematic failure mode analysis.

IIoT (Industrial Internet of Things)

Definition:
IIoT integrates networked sensors, edge devices, and cloud analytics for real-time monitoring, optimization, and automation of industrial assets.

Components:

• Smart wireless sensors
• Edge computing devices
• Cloud analytics platforms
• APIs for integration with enterprise systems

Benefits:
Enables scalable, remote monitoring and advanced predictive maintenance.

Additional Key Terms

Infrared Thermography

Non-contact measurement of temperature variations using infrared cameras to detect hot spots, insulation failures, or electrical overloads.

Lubricant Analysis (Oil Analysis)

Examination of lubricant properties (viscosity, contamination, wear particles) to assess machine wear and prevent failures.

Predictive Maintenance

Maintenance strategy that utilizes condition monitoring data and analytics to predict and prevent failures before they occur.

Root Cause Analysis (RCA)

A structured process for investigating and identifying the underlying causes of failures, guiding corrective actions.

Vibration Analysis

Measurement and interpretation of vibration levels and frequencies to detect imbalance, misalignment, or bearing defects in rotating equipment.

Practical Examples

• Aviation: Real-time monitoring of jet engine vibration and oil debris for early failure detection, in compliance with ICAO protocols.
• Manufacturing: Continuous monitoring of pumps and motors for temperature and vibration, preventing costly breakdowns.
• Energy: IIoT-based remote monitoring of wind turbines, enabling predictive maintenance and maximizing uptime.

References

• ISO 17359: Condition monitoring and diagnostics of machines – General guidelines
• ICAO Doc 9760: Maintenance procedures and safety management
• Reliabilityweb, Mobius Institute, Fluke Corporation, AVEVA, National Instruments

Condition monitoring is an evolving discipline—embracing advanced sensors, IIoT connectivity, and analytics—to deliver safer, more reliable, and cost-effective maintenance outcomes across all industries.