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Professional diploma · Machinery and electricsProfessional Diploma in Predictive Maintenance Systems
This professional diploma prepares you to lead the transformation towards proactive asset management, reducing costs and unplanned downtime. You will work with condition monitoring, vibration and oil analysis and data interpretation.
100% online800 hours8 modulesTaught in EnglishEnrol any time
What is this programme?
This professional diploma prepares you to lead the transition towards proactive asset management. It enables you to reduce costs, minimise downtime and extend the service life of equipment through data analysis and innovative technologies. It gives you the tools to apply predictive maintenance strategies, from choosing monitoring techniques to interpreting results and making strategic decisions.
The programme stands out for its coverage of the key techniques (vibration analysis, infrared thermography, oil analysis and ultrasound); its practical implementation, with predictive maintenance plans adapted to each student's sector; advanced data analysis using specialised software to detect faults early; resource optimisation to improve operational efficiency and reduce maintenance costs; and a professional diploma that documents the knowledge and skills acquired.
Programme highlights
- Predictive techniques: learn to implement and manage PdM strategies to improve asset reliability and availability.
- Advanced data analysis: use tools and software to analyse data, identify patterns and detect faults early.
- Cost reduction: strategies to lower costs, reduce downtime and improve efficiency.
- Professional diploma: a credential that supports your competence in predictive maintenance systems.
- Case studies and simulations: apply what you learn to real cases and simulations.
- Become an expert in predictive maintenance and improve the efficiency of your operations.
Study plan
01Fundamentals of predictive maintenance and asset management100 h
Principles of corrective, preventive, predictive, proactive and condition-based maintenance in industrial and maritime equipment; evolution from repair after failure to intelligent management of the asset life cycle; classification of critical assets by function, operational impact, safety, availability and cost of failure; relationship between reliability, maintainability, availability, service life and total cost of ownership; failure modes, degradation mechanisms and symptoms of deterioration; indicators such as MTBF, MTTR, technical availability and plan compliance; design of a predictive strategy aligned with operational, economic and safety objectives; comparison of corrective, preventive and predictive maintenance.
02Sensors, instrumentation and condition data acquisition100 h
Selection of sensors for vibration, temperature, pressure, flow, level, current, voltage and rotational speed; instrumentation of motors, pumps, compressors, gearboxes, bearings, generators and auxiliaries; acquisition configuration: sampling frequency, resolution and time synchronisation; installation, placement, calibration and verification of sensors in demanding environments; integration of analogue, digital and wireless signals and industrial protocols; detection of noise, interference, signal loss, drift and measurement errors; monitoring architectures for reliable data in real time and in periodic campaigns; selection and placement of vibration, temperature and pressure sensors.
03Diagnostic and condition monitoring techniques for equipment100 h
Vibration analysis to detect imbalance, misalignment, looseness, resonance and bearing defects; infrared thermography in electrical, mechanical, thermal and power distribution systems; analysis of oils, lubricants and wear particles; ultrasound to detect leaks, cavitation, electrical discharges and mechanical defects; current and electrical signature analysis in motors, generators and drives; acoustic, visual and borescope inspection as diagnostic support; integration of complementary techniques for greater accuracy and fewer false diagnoses; combined practice in vibration analysis, thermography and oil analysis.
04Signal processing and maintenance data analysis100 h
Filtering, normalisation, noise removal and outlier correction; time, frequency and time-frequency analysis of condition signals; fast Fourier transform, spectra, envelopes, harmonics and sidebands; extraction of statistical, energy and spectral features that describe degradation; baselines, alarm thresholds and severity bands based on history; management of condition, event, work order, spare parts and failure history databases; dashboards to visualise trends, anomalies, criticality and the evolution of asset condition; practice in filtering, feature extraction and spectral analysis.
05Predictive models, artificial intelligence and failure prognosis100 h
Statistics applied to estimating failures, degradation and remaining useful life; regression, classification and time series models to predict deterioration; supervised and unsupervised machine learning for anomaly detection; models to estimate the remaining useful life of critical components; training, validation and comparison of models using accuracy, sensitivity and error metrics; handling unbalanced data, scarce failure history and operational variability; assessment of explainability, robustness and reliability before using models in decisions; remaining useful life (RUL) models.
06Integration with management systems, automation and digital twins100 h
Integration of predictive maintenance with maintenance management and enterprise planning systems; automation of alarms, notifications, work orders and intervention recommendations; connection to industrial internet of things platforms and edge processing; design of digital twins that represent the behaviour of critical assets; simulation of degradation, load, failure and maintenance response scenarios; interoperability between sensors, analytics platforms, control systems and corporate databases; digital ecosystems for remote supervision of distributed assets; integration with the CMMS (computerised maintenance management system).
07Implementation, profitability and continuous improvement of predictive maintenance100 h
Diagnosis of organisational maturity for implementing predictive maintenance; prioritisation of assets by criticality, risk, cost and consequences of failure; pilot plans, proofs of concept and progressive roll-out of monitoring; economic evaluation: implementation cost, savings, reduction of downtime and return on investment; indicators of availability, reliability, compliance, diagnostic accuracy and failure reduction; staff training, change management and coordination between departments; continuous improvement through model review, feedback and strategy updates; calculating the return on investment of the predictive programme.
08Applied Final Project in Predictive Maintenance Systems100 h
Selection of an asset, installation, fleet or system with predictive monitoring needs; analysis of criticality, failure history, operating conditions and consequences of unavailability; design of the sensor, acquisition, storage and data processing architecture; development of a diagnostic and prediction model to detect degradation and estimate failures; integration with alarms, work orders, dashboards and intervention procedures; technical and economic evaluation using reliability, availability, cost and return indicators; evaluation of the proposed system in a real case; final report with methodology, results, implementation plan and continuous improvement strategy.
Duration and format
800hours in total
8modules of 100 hours
100%online, at your own pace
Openenrolment all year round
At 10 hours a week you would finish in about 80 weeks; at 20 hours a week, in about 40.
What you will learn
- Optimise asset reliability and availability.
- Implement PdM strategies to minimise unexpected failures.
- Master advanced data analysis techniques for predictive diagnosis.
- Reduce maintenance costs through failure prediction.
- Lead the transition to proactive, data-driven maintenance.
- Integrate cutting-edge technologies into maintenance management.
Career outcomes
- Predictive maintenance technician: collecting and analysing vibration, thermography, ultrasound and oil data to detect incipient faults.
- Reliability engineer: implementing predictive programmes and improving asset reliability.
- Maintenance consultant: advising companies on implementation, technology selection and training.
- Maintenance manager: leading teams and planning predictive activities.
- Maintenance data analyst: processing large volumes of data to find patterns and improvement opportunities.
- Predictive technologies specialist: developing and applying AI and machine learning to maintenance.
- Predictive maintenance trainer: delivering courses and workshops on diagnostic techniques and equipment.
- Asset manager: optimising the asset life cycle and reducing costs.
Entry requirements
- Academic or professional profile: a higher vocational qualification in industrial maintenance, an engineering degree (mechanical, electrical, electronic or related) or demonstrable professional experience in industrial maintenance.
- Prior knowledge: basic notions of thermography, vibration analysis, ultrasound and oil analysis are recommended.
- Documentation: up-to-date CV, copy of your qualification or certificate of experience, identity document or passport and, optionally, a motivation letter.
- Technical requirements (online): a computer with internet connection, an up-to-date browser and software to view PDF documents. Enrolment is open all year round; admission is provisional until the committee validates your documentation.
How to enrol
Enrol online
Complete your enrolment on this page, or apply through admissions if you prefer to talk to us first.
Receive your access
We send you the details to enter the virtual campus and start the programme.
Study at your pace
Work through the modules when it suits you and complete the programme from anywhere in the world.
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