Professional diploma800 h · Online · 8 modules
Professional 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.
Duration800 h
ModeOnline
Start1 December
Modules8
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About the programme
What is the Professional Diploma in Predictive Maintenance Systems?
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 typeProfessional diploma
ModeOnline
Duration800 hours
Modules8
Start1 December
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.
Syllabus
Syllabus of the Professional Diploma in Predictive Maintenance Systems
8 modules. Select a module to see its content.
Module 1
Fundamentals of predictive maintenance and asset management
- 1.1. Principles of corrective, preventive, predictive, proactive and condition-based maintenance applied to industrial and maritime equipment
- 1.2. Evolution of maintenance strategies from breakdown intervention to intelligent asset life-cycle management
- 1.3. Classification of critical assets by function, operational impact, safety, availability and cost of failure
- 1.4. Relationship between reliability, maintainability, availability, service life and total cost of ownership
- 1.5. Identification of failure modes, degradation mechanisms and symptoms associated with equipment deterioration
- 1.6. Maintenance indicators: mean time between failures, mean time to repair, technical availability and plan compliance
- 1.7. Design of a predictive strategy aligned with the organisation's operational, economic and safety objectives
- 1.8. Comparison of corrective, preventive and predictive maintenance
At this stage
100 hestimated workload8 wkweeks at your paceModule 2
Sensors, instrumentation and condition data acquisition
- 2.1. Selection of sensors to measure vibration, temperature, pressure, flow, level, current, voltage and rotational speed
- 2.2. Instrumentation applied to engines, pumps, compressors, gearboxes, bearings, generators and auxiliary systems
- 2.3. Configuration of data acquisition systems, sampling frequencies, resolution and time synchronisation
- 2.4. Installation, positioning, calibration and verification of sensors in demanding industrial environments
- 2.5. Integration of analogue, digital and wireless signals and industrial communication protocols
- 2.6. Detection of noise, interference, signal loss, sensor drift and measurement errors
- 2.7. Design of monitoring architectures capable of capturing reliable data in real time and in periodic campaigns
- 2.8. Selection and positioning of vibration, temperature and pressure sensors
At this stage
100 hestimated workload8 wkweeks at your paceModule 3
Diagnostic techniques and equipment condition monitoring
- 3.1. Vibration analysis to identify unbalance, misalignment, looseness, resonance and bearing defects
- 3.2. Infrared thermography applied to electrical, mechanical, thermal and power distribution systems
- 3.3. Analysis of oils, lubricants and wear particles to assess contamination, degradation and internal damage
- 3.4. Ultrasound for detecting leaks, cavitation, electrical discharges and defects in mechanical components
- 3.5. Current and electrical signature analysis to diagnose faults in motors, generators and drives
- 3.6. Acoustic, visual and borescope inspection to support condition-based diagnosis
- 3.7. Integration of complementary techniques to increase accuracy and reduce false diagnoses
- 3.8. Vibration analysis, thermography and oil analysis
At this stage
100 hestimated workload8 wkweeks at your paceModule 4
Signal processing and maintenance data analysis
- 4.1. Signal processing through filtering, normalisation, noise removal and outlier correction
- 4.2. Time-domain, frequency-domain and time-frequency analysis applied to operating condition signals
- 4.3. Fast Fourier transform, spectra, envelopes, harmonics and sidebands in machinery diagnostics
- 4.4. Extraction of statistical, energy and spectral features to describe degradation states
- 4.5. Construction of baselines, alarm thresholds and severity bands based on historical behaviour
- 4.6. Management of databases of condition, events, work orders, spare parts and failure history
- 4.7. Development of dashboards to visualise trends, anomalies, criticality and evolution of asset condition
- 4.8. Filtering, feature extraction and spectral analysis
At this stage
100 hestimated workload8 wkweeks at your paceModule 5
Predictive models, artificial intelligence and failure prognosis
- 5.1. Fundamentals of statistics applied to estimating failures, degradation and remaining useful life
- 5.2. Regression, classification and time-series models to predict equipment behaviour and deterioration
- 5.3. Application of supervised and unsupervised machine learning to anomaly detection
- 5.4. Development of models to estimate the remaining useful life of critical components
- 5.5. Training, validation and comparison of models using precision, sensitivity and predictive error metrics
- 5.6. Handling imbalanced data, scarcity of historical failures and variability of operating conditions
- 5.7. Assessment of explainability, robustness and reliability before using predictive models in maintenance decisions
- 5.8. Remaining useful life (RUL) models
At this stage
100 hestimated workload8 wkweeks at your paceModule 6
Integration with management systems, automation and digital twins
- 6.1. Integration of predictive maintenance with computerised maintenance management and enterprise resource planning systems
- 6.2. Automation of alarms, notifications, work orders and intervention recommendations
- 6.3. Connection with industrial internet of things platforms and edge computing
- 6.4. Design of digital twins to represent the physical and operational behaviour of critical assets
- 6.5. Simulation of degradation, load, failure and maintenance response scenarios
- 6.6. Interoperability between sensors, analytics platforms, control systems and corporate databases
- 6.7. Development of digital ecosystems for remote supervision and coordinated maintenance of distributed assets
- 6.8. Integration with the computerised maintenance management system (CMMS)
At this stage
100 hestimated workload8 wkweeks at your paceModule 7
Implementation, profitability and continuous improvement of predictive maintenance
- 7.1. Organisational maturity assessment for implementing predictive maintenance strategies
- 7.2. Asset prioritisation through analysis of criticality, risk, cost and consequences of failure
- 7.3. Design of pilot plans, proofs of concept and progressive roll-out of monitoring systems
- 7.4. Economic evaluation through implementation costs, expected savings, reduction of stoppages and return on investment
- 7.5. Definition of indicators for availability, reliability, compliance, diagnostic accuracy and failure reduction
- 7.6. Staff training, change management and coordination between maintenance, operations, engineering and management
- 7.7. Building continuous improvement processes through model review, feedback and strategy updates
- 7.8. Calculation of the return on investment of the predictive programme
At this stage
100 hestimated workload8 wkweeks at your paceModule 8
Applied Final Project in Predictive Maintenance Systems
- 8.1. Selection of an asset, installation, fleet or industrial system requiring predictive monitoring
- 8.2. Analysis of criticality, failure history, operating conditions and consequences associated with unavailability
- 8.3. Design of the architecture for sensors, acquisition, storage and processing of condition data
- 8.4. Development of a diagnostic and prediction model to detect degradation and estimate future failures
- 8.5. Integration of the system with alarms, work orders, dashboards and intervention procedures
- 8.6. Technical and economic evaluation using reliability, availability, cost and expected return indicators
- 8.7. Evaluation of the proposed system in a real case
- 8.8. Writing of the final report with methodology, results, implementation plan and continuous improvement strategy
At this stage
100 hestimated workload8 wkweeks at your pacePlan your pace
Duration and format of the Professional Diploma in Predictive Maintenance Systems
It is 800 hours. Tell us how many you can study each week and we will tell you when you will really finish.
4 h30 h
With 12 h per week
15
months to finish
67 weeks
weeks in total
This is the pace at which most students finish. It fits well with the instalment plan.
Faculty
Academic leadership and faculty
The Navalis teaching team: working professionals who teach and supervise the programmes.
Dra. Marina AlcoverAcademic Director and Head of Quality Assurance
Ing. Diego CifuentesSenior Lecturer in Naval Welding and Non-Destructive Testing; Head of QA/QC for Welded Joints
Cap. Javier Abaroa (MCA)Senior Lecturer in Ship Handling, Electronic Navigation (ECDIS/Radar/ARPA) and GMDSS; STCW Compliance and Bridge Resource Management Coordinator
Dr. Pau FerrerSenior Lecturer in Naval Architecture and Simulation; Director of the Hydrodynamics and CFD Laboratory
Dra. Nuria LlobregatSenior Lecturer in Marine Meteorology, SAR and Operational Safety; Navigation Risk Management Coordinator
Ing. Javier BañulsSenior Lecturer in Electrical Engineering, Automation and ETO Engineering; Electronic Integration and Engine Alarms Coordinator
Ing. Sofía MarquinaSenior Lecturer in Materials, Composites and Corrosion Protection; Coordinator of the Advanced Paints and Coatings Line
Ing. Tomás RieraSenior Lecturer in Propulsion, Alternative Energy and Hybrid Systems; Energy Performance and Vibration CoordinatorObjectives
Objectives of the Professional Diploma in Predictive Maintenance Systems
01Optimise asset reliability and availability.
02Implement PdM strategies to minimise unexpected failures.
03Master advanced data analysis techniques for predictive diagnosis.
04Reduce maintenance costs through failure prediction.
05Lead the transition to proactive, data-driven maintenance.
06Integrate cutting-edge technologies into maintenance management.
Careers
Career opportunities after the Professional Diploma in Predictive Maintenance Systems
Frequently asked questions
Questions about the Professional Diploma in Predictive Maintenance Systems
Predictive maintenance is a strategy that uses data and analysis to predict when equipment is likely to fail, enabling proactive maintenance interventions to prevent the failure and optimise performance.
Yes. The pathway includes ECDIS/Radar-ARPA/BRM with port, ocean, fog, heavy weather and SAR scenarios.
Online with live sessions; hybrid option for simulator placements/internships through agreements.
Implementing data-driven predictive maintenance strategies to anticipate and prevent failures in equipment and machinery, optimising availability and reducing costs.
Functional SMCP is recommended. We provide support materials for standard phraseology.
Yes, with a related qualification or experience in maritime/port operations. The admission interview will confirm your fit.
Optional (3–6 months) through Companies & Partnerships and the Alumni Network.
Simulator exercises (rubrics), passage plans, SOPs, checklists, micro-tests and an applied master's thesis.
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Online admission
Admission process
No waiting for a call back. If your documents are in order, you can enrol in the same session.
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.
- 01ApplicationYour details, background and documents.
- 02Instant reviewWe check that everything is complete and in order.
- 03EnrolmentChoose full payment or instalments and reserve your place.
Start my applicationFrom ₹46,200 deposit
Admission is provisional until the committee validates the documents provided. If you do not meet the requirements, the full amount is refunded.
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