Course Description
Condition monitoring generates vast streams of vibration, temperature, acoustic, and oil-analysis data — and artificial intelligence is transforming how that data is interpreted. This course shows reliability and condition monitoring professionals how AI and machine learning enhance fault detection, diagnosis, and prognosis beyond traditional threshold-based methods.
Participants learn how machine learning models detect subtle patterns in condition-monitoring data, classify fault types automatically, and predict remaining useful life. The course bridges established condition monitoring practice (vibration, thermography, oil analysis) with modern AI techniques, making it directly relevant to professionals extending existing CM programs with intelligent analytics.
What you will achieve
By the end of this training course, participants will be able to:
- Explain how AI and machine learning enhance condition monitoring
- Prepare and process condition monitoring data (vibration, thermal, acoustic, oil)
- Apply anomaly detection to time-series condition data
- Use machine learning to classify and diagnose fault types
- Estimate remaining useful life using AI models
- Integrate AI analytics with existing condition monitoring programs
- Interpret and validate AI diagnostic outputs
- Understand the limitations and pitfalls of AI in condition monitoring
How the course is delivered
The course combines condition monitoring domain knowledge with practical machine learning explanation, using real vibration, thermal, and acoustic datasets. Participants work through anomaly detection and fault classification exercises.
The programme is designed for condition monitoring professionals without a data science background, focusing on applied understanding and integration with existing CM technologies.
Designed for
This training course is ideal for:
- Condition monitoring analysts and technicians
- Reliability and maintenance engineers
- Vibration, thermography, and oil analysis specialists
- Engineers extending CM programs with analytics
- Technical staff bridging condition monitoring and data science
Daily programme
- Review of condition monitoring technologies
- Limitations of threshold-based analysis
- How AI enhances condition monitoring
- Types of machine learning for CM
- Data sources: vibration, thermal, acoustic, oil
- Data acquisition and quality
- Feature extraction from sensor signals
- Time and frequency domain features
- Anomaly detection principles
- Unsupervised methods for CM
- Detecting subtle degradation
- Baseline modelling and drift
- Fault classification models
- Diagnosing bearing, gear, and imbalance faults
- Remaining useful life (RUL) estimation
- Prognostic models
- Integrating AI with CM programs
- Validating and trusting AI outputs
- Limitations and pitfalls
- Case study workshop and review
Certification & accreditation
BII Certificate of Completion
BII Certificate of Completion Upon successful completion, participants receive a BII Development Institute Certificate of Completion with a unique reference code that is independently verifiable. Our certificates are recognised internationally and reflect successful completion of your chosen programme.
