Dr. Marina AlcoverAcademic director focused on measurable results and operational excellence in maritime education. Integrates IMO/STCW regulations and ISO 21001/9001 f
Diploma · 800 h · 8 modules
Diploma in Predictive Maintenance Systems
The Diploma in Predictive Maintenance SystemsThis program prepares you to lead the transformation towards proactive asset management. Learn to reduce costs, minimize downtime, and extend the useful life of your equipment throug
Duration800 h
FormatOnline
Starts30 November
Places20 / 20
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Programme overview
What is the Diploma in Predictive Maintenance Systems?
The Diploma in Predictive Maintenance Systems
This program prepares you to lead the transformation towards proactive asset management. Learn to reduce costs, minimize downtime, and extend the useful life of your equipment through data analysis and the application of innovative technologies. This program provides you with the tools to implement effective PdM strategies, from selecting monitoring techniques to interpreting results and making strategic decisions.
This program prepares you to lead the transformation towards proactive asset management.
Differential Advantages
- Mastery of Key Techniques: Vibration analysis, infrared thermography, oil analysis, ultrasound.
- Practical Implementation: Development of predictive maintenance plans tailored to your industry.
- Advanced Data Analysis: Use of specialized software for early fault detection.
- Resource Optimization: Improved operational efficiency and reduced maintenance costs.
- Professional Certification: Validation of your knowledge and skills in predictive maintenance.
Programme type
Diploma
Study mode
Online
Duration
800 hours
Modules
8
Start date
26-11-2026
Programme highlights
- Predictive Techniques Mastery: Learn to implement and manage predictive maintenance (PdM) strategies to optimize the reliability and availability of your assets.
- Advanced Data Analysis: Master the use of tools and software for data analysis, pattern identification, and early failure detection.
- Cost Reduction and Optimization: Implement strategies that allow you to reduce maintenance costs, minimize downtime, and improve operational efficiency.
- Professional Certification: Obtain a recognized diploma that validates your knowledge and skills in predictive maintenance systems, boosting your professional development.
- Practical Cases and Simulations: Apply the acquired knowledge Through real-world case studies and practical simulations, guaranteeing effective applied learning.
- Boost the efficiency of your operations and become an expert in predictive maintenance.
Curriculum
Programme Structure
The programme is structured in 8 modules, listed below in order of study.
01
Basics of predictive maintenance and asset management
- Principles of corrective, preventive, predictive, proactive and condition-based maintenance applied to industrial and maritime equipment
- Evolution of maintenance strategies from breakdown intervention to intelligent asset life cycle management
- Classification of critical assets according to function, operational impact, security, availability and cost of failure
- Relationship between reliability, maintainability, availability, useful life and total cost of ownership
- Identification of failure modes, degradation mechanisms and symptoms associated with equipment deterioration
- Maintenance indicators: Average time between failures, average repair time, technical availability and compliance with the plan
- Design of a predictive strategy aligned with the organisation's operational, economic and security objectives
- Comparison between corrective, preventive and predictive maintenance
02
Sensors, Instrumentation and Acquisition of Condition Data
- Selection of sensors to measure vibration, temperature, pressure, flow rate, level, current, voltage and speed of rotation
- Instrumentation applied to motors, pumps, compressors, reducers, bearings, generators and auxiliary systems
- Configuration of data acquisition systems, sampling frequencies, resolution and time sync
- Installation, location, calibration and verification of sensors in demanding industrial environments
- Integration of analog, digital, wireless signals and industrial communication protocols
- Noise detection, interference, signal loss, sensor drift and measurement errors
- Design of monitoring architectures capable of capturing reliable data in real time and in periodic campaigns
- Selection and location of vibration, temperature and pressure sensors
03
Diagnostic techniques and monitoring of equipment status
- Vibration analysis to identify imbalance, misalignment, clearances, resonance and bearing defects
- Infrared thermography applied to electrical, mechanical, thermal and energy distribution systems
- Analysis of oils, lubricants and wear particles to evaluate contamination, degradation and internal damage
- Ultrasounds for leak detection, cavitation, electrical discharges and defects in mechanical elements
- Current analysis and electrical signature to diagnose engines, generators and drives failures
- Acoustic, visual, and boroscopic inspection as a condition-based diagnostic support
- Integration of complementary techniques to increase precision and reduce false diagnoses
- Vibration Analysis, Thermography and Oil Analysis
04
Signal processing and maintenance data analysis
- Treatment of signals by filtering, normalization, elimination of noise and correction of outliers
- Temporal, frequency and time-frequency analysis applied to operating condition signals
- Fast Fourier transform, spectra, envelopes, harmonics and sidebands in machinery diagnosis
- Extraction of statistical, energetic and spectral characteristics to describe degradation states
- Construction of baselines, alarm thresholds and bands of severity according to historical behaviour
- Management of condition databases, events, work orders, spare parts and failure history
- Development of dashboards to visualise trends, anomalies, criticality and evolution of the status of assets
- Filtering, feature extraction and spectral analysis
05
Predictive models, artificial intelligence and fault forecast
- Statistical fundamentals applied to the estimation of failures, degradation and remaining useful life
- Regression models, classification and time series to predict behaviour and deterioration of equipment
- Supervised and unsupervised machine learning application in anomaly detection
- Development of models to estimate the remaining useful life of critical components
- Training, validation and comparison of models using precision, sensitivity and predictive error metrics
- Unbalanced data management, shortage of historical failures and variability of operating conditions
- Explainability, robustness and reliability assessment before using predictive models in maintenance decisions
- Remaining Life Models (RUL)
06
Integration with management systems, automation and digital twins
- Integration of predictive maintenance with computerized maintenance management and business planning systems
- Automation of alarms, warnings, work orders and intervention recommendations
- Connecting to Industrial Internet of Things and Edge Processing Platforms
- Design of digital twins to represent the physical and operational behaviour of critical assets
- Simulation of degradation, loading, failure and maintenance response scenarios
- Interoperability between sensors, analytical platforms, control systems and corporate databases
- Development of digital ecosystems for remote monitoring and coordinated maintenance of distributed assets
- Integration with the Maintenance Management System (GMAO)
07
Implementation, profitability and continuous improvement of predictive maintenance
- Organisational maturity diagnosis to implement predictive maintenance strategies
- Prioritization of assets through analyses of criticality, risk, cost and consequences of failure
- Design of pilot plans, concept tests and progressive deployment of monitoring systems
- Economic evaluation through implementation costs, expected savings, reduction of stops and return on investment
- Definition of indicators on availability, reliability, compliance, diagnostic accuracy and failure reduction
- Staff training, change management and coordination between maintenance, operations, engineering and management
- Construction of continuous improvement processes through model review, feedback and update strategies
- Calculation of the return on investment of the predictive program
08
Final Applied Predictive Maintenance Systems Project
- Selection of an asset, installation, fleet or industrial system with predictive monitoring needs
- Criticality analysis, failure history, operating conditions and consequences associated with unavailability
- Design of the sensor architecture, acquisition, storage and processing of condition data
- Development of a diagnosis and prediction model to detect degradation and estimate future failures
- System integration with alarms, work orders, dashboards and intervention procedures
- Technical and economic evaluation through indicators of reliability, availability, cost and expected return
- Evaluation of the proposed system in a real case
- Drafting of the final memory with methodology, results, implementation plan and continuous improvement strategy
Workload
Study Time and Workload
800 hours of study, online and at your own pace. Move the slider to the hours a week you can really put in.
Hours a week8
Weeks to finish—
You finish around—
Who it is for
Maintenance engineers looking to implement predictive strategies and optimize asset management.Maintenance technicians interested in deepening their knowledge of vibration analysis, thermography, and ultrasound techniques.Plant managers and maintenance supervisors who want to reduce costs, minimize unscheduled downtime, and improve the reliability of their equipment.Engineering students and
Faculty
Academic direction and faculty
The teaching team at Navalis: working professionals who deliver and oversee the institute's programmes.
Eng. Diego Cifuentes
Cap. Javier Abaroa (MCA)
Dr. Pau Ferrer
Dr. Nuria Llobregat
Eng. Javier Bañuls
Eng. Sofía Marquina
Eng. Tomás Riera
Verified reviews
Student Reviews
Reviews collected and verified by Opynio, not written by us.
Outcomes
Learning Outcomes
- 01Optimize asset reliability and availability
- 02Implement PdM strategies to minimize unexpected failures
- 03Mastering advanced data analysis techniques for predictive diagnostics
- 04Reduce maintenance costs by predicting failures
- 05Leading the transition to proactive, data-driven maintenance
- 06Integrate cutting-edge technologies into maintenance management
Careers
Career Opportunities
01Predictive Maintenance Technician: Collection and analysis of vibration, thermography, ultrasound, and oil analysis data to detect incipient equipment failures.
02Reliability Engineer: Implementation of predictive maintenance programs, optimization of maintenance strategies, and improvement of asset reliability.
03Maintenance Consultant: Advising companies on the implementation of predictive maintenance systems, technology selection, and personnel training.
04Maintenance Manager: Management of maintenance teams, planning of predictive maintenance activities, and monitoring of equipment condition.
05Maintenance Data Analyst: Processing and analysis of large volumes of maintenance data to identify patterns, trends, and opportunities for improvement.
06Predictive Maintenance Technology Specialist: Development and application of new predictive maintenance technologies. such as artificial intelligence and machine learning.
07Predictive Maintenance Trainer: Delivering courses and workshops on predictive maintenance techniques and the use of diagnostic equipment.
08Asset Manager: Optimizing the asset lifecycle through the application of predictive maintenance strategies and cost reduction.
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Questions
Entry Requirements
Predictive maintenance is a strategy that uses data and analysis to predict when equipment is likely to fail, allowing proactive maintenance interventions to prevent failure and optimize performance.
Yes. The itinerary includes ECDIS/Radar-ARPA/BRM with harbor, ocean, fog, storm, and SAR scenarios.
Online with live sessions; hybrid option for simulator/practical placements through agreements.
Implement data-driven predictive maintenance strategies to anticipate and prevent equipment and machinery failures, optimizing availability and reducing costs.
Recommended functional SMCP. We offer support materials for standard phraseology.
Yes, with a relevant degree or experience in maritime/port operations. The admissions interview will confirm suitability.
Optional (3–6 months) through Companies & Collaborations and the Alumni Network.
Simulator practice (rubrics), defeat plans, SOPs, checklists, micro-tests and applied TFM.
A degree from Navalis Magna Institute + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
Online admissions
Admissions Process
No waiting for a phone call. If your documents are complete, you can pay in the same session.
Entry requirements
Academic/Professional Profile:Higher Technician Diploma in Industrial Maintenance, Engineering (Mechanical, Electrical, Electronic, etc.) or related qualification; or demonstrable professional experience in industrial maintenance areas.Previous Knowledge:Basic knowledge of thermography, vibration analysis, ultrasound, and oil analysis is recommended.Documentation:Updated CV, copy of degree or work experience certificate, ID/Passport, cover letter (optional).Technical Requirements (for online, if applicable):Computer with internet access, updated web browser, document viewing
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