Remote Engine Monitoring Course
Why this course?
The Remote Engine Monitoring Course
This course provides you with the essential skills to optimize performance and extend the lifespan of your assets. Learn to interpret real-time data, identify anomalies, and predict failures before they occur. Master predictive analytics techniques, the use of IoT sensors, and the most advanced data management platforms. This program will make you an expert in proactive maintenance and operational efficiency.
This course provides you with the essential skills to optimize performance and extend the lifespan of your assets.
Differential Advantages
- Practical Implementation: sensor configuration, data transmission, and visualization on customized dashboards.
- Predictive Analytics: application of machine learning algorithms for early problem detection.
- Real-World Case Studies: analysis of concrete examples from different industries and engine types.
- Maintenance Optimization: strategies to reduce costs, minimize downtime, and improve planning.
- Access to Experts: personalized support and Q&A with industry professionals.
- Modality: Online
- Level: Cursos
- Hours: 150 H
- Start date: 26-07-2026
Availability: 1 in stock
Who is it aimed at?
- Maintenance engineers seeking to optimize engine performance and availability through real-time data analysis.
- Plant supervisors and operations managers interested in reducing operating costs, minimizing unplanned downtime, and improving overall efficiency.
- Maintenance service providers wanting to offer proactive and predictive solutions to their clients, differentiating themselves in the market with cutting-edge technology.
- Instrumentation and control technicians seeking to expand their skills in remote fault diagnosis, calibration, and engine parameter tuning.
- Mechanical engineering students and related fields wanting to gain practical knowledge of the latest trends in condition monitoring and predictive maintenance.
Learning flexibility
Access the content At your own pace, with practical examples and specialized technical support to answer all your questions.
Objectives and competencies
Implement early warnings of failures:
Monitor critical parameters (vibration, temperature, pressure) and establish alert thresholds based on historical data and manufacturer recommendations.
Optimize engine performance through data analysis:
Through predictive analysis and fine-tuning of operating parameters, maximize fuel consumption efficiency and minimize pollutant emissions.
Reduce maintenance costs through predictive diagnostics:
Implement data analysis techniques (Machine Learning, IoT) to predict failures, optimize preventive maintenance plans, and reduce unplanned downtime.
Centralize engine status information for efficient management:
“Implement a robust SCADA system integrated with the PMS and the predictive maintenance system.”
Ensure continuous engine availability:
“Implement preventive, predictive and corrective maintenance plans, managing resources and optimizing spare parts logistics.”
Extending engine life through early problem detection:
“Monitor key parameters (temperature, oil pressure, vibrations) and interpret data to identify deviations and plan preventive maintenance.”
Curriculum - Modules
- Comprehensive Maritime Incident Management: protocols, roles, and chain of command for coordinated response
- Operational Planning and Execution: briefing, routes, weather windows, and go/no-go criteria
- Rapid Risk Assessment: criticality matrix, scene control, and decision-making under pressure
- Operational Communication: VHF/GMDSS, standardized reports, and inter-agency liaison
- Tactical Mobility and Safe Boarding: RHIB maneuvers, approach, mooring, and recovery
- Equipment and Technologies: PPE, signaling, satellite tracking, and field data logging
- Immediate Care of the Affected: primary assessment, hypothermia, trauma, and stabilization for evacuation
- Adverse Environmental Conditions: swell, Visibility, flows, and operational mitigation
Simulation and training: critical scenarios, use of VR/AR, and exercises with performance metrics
Documentation and continuous improvement: lessons learned, indicators (MTTA/MTTR), and SOP updates
- Introduction to Predictive Engine Diagnostics: Concepts and Benefits
- Fundamentals of Thermodynamics: Engine Cycles, Efficiency, and Losses
- Vibration Analysis: Types of Vibrations, Sensors, and Measurement Techniques
- Oil Analysis: Oil Properties, Contaminants, and Component Wear
- Infrared Thermography: Applications in Engine Fault Detection
- Exhaust Gas Analysis: Indicators of Combustion and Engine Problems
- Monitoring Operating Parameters: Pressure, Temperature, Flow, and Performance
- Combustion Optimization: Parameter Adjustment and Efficiency Improvement
- Condition-Based Maintenance: Planning and execution of maintenance tasks.
Case studies and practical applications in different types of engines.
‘
- Introduction to Predictive Diagnostics: Concepts, Benefits, and Applications
- Sensor Fundamentals: Types, Characteristics, Calibration, and Maintenance
- Data Acquisition: Hardware, Software, Communication Protocols, and Storage
- Signal Processing Techniques: Filtering, Fourier Transform, and Statistical Analysis
- Vibration Analysis: Fundamentals, Instrumentation, Interpretation, and Diagnosis
- Infrared Thermography: Principles, Equipment, and Applications in Predictive Maintenance
- Oil Analysis: Sampling, Testing, Interpretation of Results, and Corrective Actions
- Remote Control and Monitoring: System Architecture, Wireless Communication Protocols, and Cybersecurity
- Implementation of a Predictive Diagnostic Program: Planning, Resources, Metrics, and Continuous Improvement
- Case Studies and Practical Applications in Various Industries
‘
- Introduction to Predictive Engine Diagnostics: Concepts, Benefits, and Applications
- Fundamentals of Infrared Thermography: Principles, Equipment, and Measurement Techniques
- Vibration Analysis: Data Acquisition, Spectra, Amplitude, and Frequency
- Oil Analysis: Types of Analysis, Interpretation of Results, and Corrective Actions
- Acoustic Monitoring: Identification of Abnormal Noise, Leak Detection, and Wear Detection
- Sensors and Remote Data Acquisition Systems: Types, Installation, and Configuration
- Communication and Data Transmission: Protocols, Networks, and Security
- Software Platforms for Diagnostics
Predictive: Functionalities, Integration, and Visualization
Artificial Intelligence and Machine Learning Algorithms: Applications in Predictive Diagnostics
Case Studies and Best Practices in Remote Engine Optimization
‘
- Introduction to Predictive Diagnostics: Concepts, benefits, and applications in motors.
- Fundamentals of electric motors: Types, components, and operating principles.
- Sensors and monitoring systems: Types of sensors, installation, and configuration.
- Data acquisition and processing: Platforms, communication protocols, and signal preprocessing.
- Vibration analysis: Analysis techniques, fault identification, and relevant parameters.
- Temperature analysis: Measurement, interpretation, and anomaly detection.
- Current and voltage analysis: Analysis techniques, identification of electrical faults and harmonics.
- Artificial intelligence and machine learning techniques: Application in predictive diagnostics.
- Remote control platforms: Architecture, security, and communication.
- Implementation of maintenance strategies Predictive: Planning, execution, and evaluation.
‘
- System Architecture and Components: Structural design, materials, and subsystems (mechanical, electrical, electronic, and fluid) with selection and assembly criteria for marine environments
- Fundamentals and Principles of Operation: Physical and engineering foundations (thermodynamics, fluid mechanics, electricity, control, and materials) that explain performance and operating limits
- Safety and Environmental (SHE): Risk analysis, PPE, LOTO, hazardous atmospheres, spill and waste management, and emergency response plans
- Applicable Regulations and Standards: IMO/ISO/IEC requirements and local regulations;
- Conformance criteria, certification, and best practices for operation and maintenance
- Inspection, testing, and diagnostics: Visual/dimensional inspection, functional testing, data analysis, and predictive techniques (vibration, thermography, fluid analysis) to identify root causes
- Preventive and predictive maintenance: Hourly/cycle/seasonal plans, lubrication, adjustments, calibrations, consumable replacement, post-service verification, and operational reliability
- Instrumentation, tools, and metrology: Measuring and testing equipment, diagnostic software, calibration and traceability; selection criteria, safe use, and storage
- Onboard integration and interfaces: Mechanical, electrical, fluid, and data compatibility; Sealing and watertightness, EMC/EMI, corrosion protection, and interoperability testing.
Quality, acceptance testing, and commissioning: process and materials control, FAT/SAT, bench and sea trials, go/no-go criteria, and evidence documentation.
Technical documentation and integrated practice: logs, checklists, reports, and a complete case study (safety → diagnosis → intervention → verification → report) applicable to any system.
Plan de estudio - Módulos
- Comprehensive Maritime Incident Management: protocols, roles, and chain of command for coordinated response
- Operational Planning and Execution: briefing, routes, weather windows, and go/no-go criteria
- Rapid Risk Assessment: criticality matrix, scene control, and decision-making under pressure
- Operational Communication: VHF/GMDSS, standardized reports, and inter-agency liaison
- Tactical Mobility and Safe Boarding: RHIB maneuvers, approach, mooring, and recovery
- Equipment and Technologies: PPE, signaling, satellite tracking, and field data logging
- Immediate Care of the Affected: primary assessment, hypothermia, trauma, and stabilization for evacuation
- Adverse Environmental Conditions: swell, Visibility, flows, and operational mitigation
Simulation and training: critical scenarios, use of VR/AR, and exercises with performance metrics
Documentation and continuous improvement: lessons learned, indicators (MTTA/MTTR), and SOP updates
- Introduction to Predictive Engine Diagnostics: Concepts and Benefits
- Fundamentals of Thermodynamics: Engine Cycles, Efficiency, and Losses
- Vibration Analysis: Types of Vibrations, Sensors, and Measurement Techniques
- Oil Analysis: Oil Properties, Contaminants, and Component Wear
- Infrared Thermography: Applications in Engine Fault Detection
- Exhaust Gas Analysis: Indicators of Combustion and Engine Problems
- Monitoring Operating Parameters: Pressure, Temperature, Flow, and Performance
- Combustion Optimization: Parameter Adjustment and Efficiency Improvement
- Condition-Based Maintenance: Planning and execution of maintenance tasks.
Case studies and practical applications in different types of engines.
‘
- Introduction to Predictive Diagnostics: Concepts, Benefits, and Applications
- Sensor Fundamentals: Types, Characteristics, Calibration, and Maintenance
- Data Acquisition: Hardware, Software, Communication Protocols, and Storage
- Signal Processing Techniques: Filtering, Fourier Transform, and Statistical Analysis
- Vibration Analysis: Fundamentals, Instrumentation, Interpretation, and Diagnosis
- Infrared Thermography: Principles, Equipment, and Applications in Predictive Maintenance
- Oil Analysis: Sampling, Testing, Interpretation of Results, and Corrective Actions
- Remote Control and Monitoring: System Architecture, Wireless Communication Protocols, and Cybersecurity
- Implementation of a Predictive Diagnostic Program: Planning, Resources, Metrics, and Continuous Improvement
- Case Studies and Practical Applications in Various Industries
‘
- Introduction to Predictive Engine Diagnostics: Concepts, Benefits, and Applications
- Fundamentals of Infrared Thermography: Principles, Equipment, and Measurement Techniques
- Vibration Analysis: Data Acquisition, Spectra, Amplitude, and Frequency
- Oil Analysis: Types of Analysis, Interpretation of Results, and Corrective Actions
- Acoustic Monitoring: Identification of Abnormal Noise, Leak Detection, and Wear Detection
- Sensors and Remote Data Acquisition Systems: Types, Installation, and Configuration
- Communication and Data Transmission: Protocols, Networks, and Security
- Software Platforms for Diagnostics
Predictive: Functionalities, Integration, and Visualization
Artificial Intelligence and Machine Learning Algorithms: Applications in Predictive Diagnostics
Case Studies and Best Practices in Remote Engine Optimization
‘
- Introduction to Predictive Diagnostics: Concepts, benefits, and applications in motors.
- Fundamentals of electric motors: Types, components, and operating principles.
- Sensors and monitoring systems: Types of sensors, installation, and configuration.
- Data acquisition and processing: Platforms, communication protocols, and signal preprocessing.
- Vibration analysis: Analysis techniques, fault identification, and relevant parameters.
- Temperature analysis: Measurement, interpretation, and anomaly detection.
- Current and voltage analysis: Analysis techniques, identification of electrical faults and harmonics.
- Artificial intelligence and machine learning techniques: Application in predictive diagnostics.
- Remote control platforms: Architecture, security, and communication.
- Implementation of maintenance strategies Predictive: Planning, execution, and evaluation.
‘
- System Architecture and Components: Structural design, materials, and subsystems (mechanical, electrical, electronic, and fluid) with selection and assembly criteria for marine environments
- Fundamentals and Principles of Operation: Physical and engineering foundations (thermodynamics, fluid mechanics, electricity, control, and materials) that explain performance and operating limits
- Safety and Environmental (SHE): Risk analysis, PPE, LOTO, hazardous atmospheres, spill and waste management, and emergency response plans
- Applicable Regulations and Standards: IMO/ISO/IEC requirements and local regulations;
- Conformance criteria, certification, and best practices for operation and maintenance
- Inspection, testing, and diagnostics: Visual/dimensional inspection, functional testing, data analysis, and predictive techniques (vibration, thermography, fluid analysis) to identify root causes
- Preventive and predictive maintenance: Hourly/cycle/seasonal plans, lubrication, adjustments, calibrations, consumable replacement, post-service verification, and operational reliability
- Instrumentation, tools, and metrology: Measuring and testing equipment, diagnostic software, calibration and traceability; selection criteria, safe use, and storage
- Onboard integration and interfaces: Mechanical, electrical, fluid, and data compatibility; Sealing and watertightness, EMC/EMI, corrosion protection, and interoperability testing.
Quality, acceptance testing, and commissioning: process and materials control, FAT/SAT, bench and sea trials, go/no-go criteria, and evidence documentation.
Technical documentation and integrated practice: logs, checklists, reports, and a complete case study (safety → diagnosis → intervention → verification → report) applicable to any system.
- Introduction to Engine Monitoring and Predictive Diagnostics: Objectives, Benefits, and Scope.
- Fundamentals of Thermodynamics and Combustion in Engines: Thermodynamic Cycles, Types of Combustion, and Efficiency.
- Vibration Analysis: Data Collection, FFT, Spectrum Interpretation, and Fault Detection.
- Oil Analysis: Types of Analysis, Interpretation of Results, and Detection of Wear and Contamination.
- Infrared Thermography: Principles, Equipment, Measurement Techniques, and Identification of Hot Spots and Electrical/Mechanical Faults.
- Ultrasound: Fundamentals, Leak Detection, Friction, and Cavitation.
- Data Acquisition and Processing Systems: Sensors, DAQ, and Data Analysis and Management Software.
- Diagnosis of common faults: Wear, misalignment, imbalance, lubrication problems, bearing and gear failures.
- Predictive maintenance strategies: Planning, scheduling, execution, and evaluation of the maintenance program.
- Practical case studies: Analysis of real data, fault identification, recommendations, and optimization of engine performance.
‘
- Introduction to Predictive Diagnostics: Concepts, Benefits, and Applications in Engines
- IoT Fundamentals: Architecture, Communication Protocols, and Security
- Sensors for Engines: Types, Characteristics, Installation, and Calibration
- Data Acquisition and Processing: Filtering, Cleaning, and Normalization Techniques
- Energy Efficiency in Engines: Key Parameters, Standards, and Regulations
- Predictive Modeling: Machine Learning Algorithms for Fault Detection
- Vibration Analysis: FFT Techniques, Spectrum Interpretation, and Fault Diagnosis
- Infrared Thermography: Hot Spot Detection, Analysis, and Applications in Engines
- Implementation of Predictive Diagnostic Systems: Design, Deployment, and Maintenance
Case Studies: Real-world applications and industry success stories
‘
- Introduction to Predictive Diagnostics: Concepts, Benefits, and Applications in Engines
- Sensors and Data Acquisition: Types, Location, Calibration, and Maintenance
- Vibration Analysis: Spectra, Patterns, and Identification of Mechanical Faults
- Infrared Thermography: Hot Spot Detection and Thermal Pattern Analysis
- Oil Analysis: Properties, Contaminants, Wear, and Lubrication
- Exhaust Gas Analysis: Combustion, Efficiency, Emissions, and Contaminants
- Predictive Modeling: Machine Learning Algorithms, Training, and Validation
- Remote Communication: Protocols, Security, and Data Transmission
- Data Visualization: Dashboards, Reports, and Real-Time Alerts
- Remote Engine Optimization: Adjustments, Parameters, and Strategies control
‘
- Fundamentals of combustion in internal combustion engines: thermodynamic principles and stoichiometry.
- Advanced sensors and actuators: types, operation, calibration, and diagnostics.
- Data acquisition: DAQ systems, communication protocols (CAN bus, Ethernet).
- Signal analysis in the time and frequency domains: Fourier transform, wavelets.
- Monitoring of key parameters: cylinder pressure, vibrations, exhaust gas temperature.
- Engine modeling: black box models, physical models, and their application in monitoring.
- Model-based diagnostics: fault detection, parameter identification, and condition estimation.
- Performance optimization: air/fuel ratio control, ignition strategies, and engine management Loading.
- Artificial intelligence techniques: neural networks, genetic algorithms, and their application in monitoring and optimization.
- Case studies: monitoring and optimization of motors in industrial and automotive applications.
‘
Career opportunities
- Predictive Maintenance Technician: Analyzing engine data to anticipate failures and optimize maintenance.
- Remote Diagnostic Engineer: Detecting and resolving engine problems remotely, minimizing downtime.
- Energy Efficiency Consultant: Optimizing engine performance to reduce fuel consumption and emissions.
- Control Systems Specialist: Implementing and managing remote engine monitoring systems.
- Operations Supervisor: Monitoring engine performance in real time to ensure efficiency and safety.
- Development Researcher: Researching and developing new technologies and techniques for remote engine monitoring.
- Technical Trainer: Training personnel in the use and maintenance of remote engine monitoring systems.
- Technical Sales: Advising and selling remote engine monitoring solutions to companies.
“`
Admission requirements
Academic/professional profile:
Degree/Bachelor's degree in Nautical Science/Maritime Transport, Naval/Marine Engineering, or a related field; or proven professional experience in bridge/operations.
Language proficiency:
Recommended functional maritime English (SMCP) for simulations and technical materials.
5. Induction
Updated resume, copy of degree or seaman's book, ID card/passport, letter of motivation.
Technical requirements (for online):
Equipment with camera/microphone, stable connection, ≥ 24” monitor recommended for ECDIS/Radar-ARPA.
Admission process and dates
1. Online
application
(form + documents).
2. Academic review and interview
(profile/objectives/schedule compatibility).
3. Admission decision
(+ scholarship proposal if applicable).
4. Reservation of place
(deposit) and registration.
5. Induction
(access to campus, calendars, simulator guides).
Scholarships and grants
- Total Mastery: Learn to diagnose and troubleshoot motor problems remotely, optimizing performance and minimizing downtime.
- Advanced Tools: Master the use of specialized software and sensors for real-time data collection and analysis.
- Efficiency and Savings: Reduce maintenance costs and unnecessary travel by anticipating failures and performing precise interventions.
- Professional Certification: Earn a certificate that validates your remote monitoring skills, boosting your career.
- Practical Application: Real-world case studies and simulations so you can immediately apply your knowledge in real-world environments. industrial.
Testimonials
I implemented a remote monitoring system for a fleet of 50 diesel engines, which reduced unforeseen failures by 80% and maintenance costs by 25% in the first year.
I applied the knowledge from the Marine Electronics and Automation course to design an automated ballast control system that reduced operating time by 30% and minimized the risk of human error on a tanker, resulting in greater efficiency and safety in maritime operations.
I implemented a remote monitoring system that reduced unplanned downtime of our truck fleet’s engines by 15%, generating annual savings of $80,000 in maintenance costs.
I implemented a remote monitoring system that reduced engine downtime by 15% and allowed for predicting failures a week in advance, saving $50,000 in maintenance costs during the first quarter.
Frequently asked questions
Remote engine monitoring involves the use of sensors and communication systems to track the performance and condition of an engine from a remote location, enabling early detection of potential problems and predictive maintenance.
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.
It allows for the early detection of problems, reducing downtime and maintenance 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 University + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
- Fundamentals of combustion in internal combustion engines: thermodynamic principles and stoichiometry.
- Advanced sensors and actuators: types, operation, calibration, and diagnostics.
- Data acquisition: DAQ systems, communication protocols (CAN bus, Ethernet).
- Signal analysis in the time and frequency domains: Fourier transform, wavelets.
- Monitoring of key parameters: cylinder pressure, vibrations, exhaust gas temperature.
- Engine modeling: black box models, physical models, and their application in monitoring.
- Model-based diagnostics: fault detection, parameter identification, and condition estimation.
- Performance optimization: air/fuel ratio control, ignition strategies, and engine management Loading.
- Artificial intelligence techniques: neural networks, genetic algorithms, and their application in monitoring and optimization.
- Case studies: monitoring and optimization of motors in industrial and automotive applications.
‘
Request information
- Complete the Application Form
- Attach your CV/Qualifications (if you have them to hand).
- Indicate your preferred cohort (January/May/September) and whether you want the hybrid option with simulator sessions.
Teachers
Eng. Tomás Riera
Full Professor
Eng. Tomás Riera
Full Professor
Naval engineer specializing in the selection, integration, and optimization of propulsion systems for yachts, high-speed craft, and service vessels.
Eng. Sofía Marquina
Full Professor
Eng. Sofía Marquina
Full Professor
Materials engineer specializing in marine composites and corrosion protection systems for ships, yachts, and port structures.
Eng. Javier Bañuls
Full Professor
Eng. Javier Bañuls
Full Professor
Naval electrical engineer with over fifteen years of experience in the design, integration, and operation of power and automation systems.
Dr. Nuria Llobregat
Full Professor
Dr. Nuria Llobregat
Full Professor
Specialist in meteorological and operational decision-making for coastal and offshore navigation, integrating wind, wave, and current models.
Dr. Pau Ferrer
Full Professor
Dr. Pau Ferrer
Full Professor
Naval engineer with a PhD in applied hydrodynamics and over a decade of experience designing high-performance hulls and marine structures.
Cap. Javier Abaroa (MCA)
Full Professor
Cap. Javier Abaroa (MCA)
Full Professor
MCA-certified captain with command of ocean-going vessels, specializing in advanced maneuvering, navigation management, and bridge safety.