Advanced condition-based maintenance platform with real-time sensor monitoring, predictive analytics, anomaly detection, and IoT integration for proactive maritime equipment maintenance.
Connected Sensors
Anomalies Today
Prediction Accuracy
Failures This Year
Leverage machine learning algorithms to predict equipment failures before they occur. Analyze sensor patterns, detect anomalies, and receive actionable alerts to prevent costly breakdowns and unplanned downtime.
AI algorithms trained on maritime equipment data to identify failure patterns and degradation trends
Real-time detection of abnormal sensor readings and equipment behavior patterns
Predict equipment remaining useful life (RUL) to optimize replacement and overhaul timing
Advanced sensor integration, real-time analytics, and predictive algorithms for condition-based maritime maintenance.
Connect vibration, temperature, pressure, and flow sensors with seamless IoT gateway integration.
Live dashboards displaying sensor data, trends, and equipment health status across your fleet.
FFT spectrum analysis, bearing defect detection, and rotating machinery diagnostics.
Import oil analysis reports and correlate with sensor data for comprehensive diagnostics.
Configurable alarm thresholds with instant notifications when parameters exceed limits.
Long-term sensor data storage for trend analysis, reporting, and machine learning training.
Transform raw sensor data into actionable maintenance insights with our intelligent CBM pipeline.
Continuous data streaming from IoT sensors monitoring vibration, temperature, pressure, and other parameters.
Real-time signal processing, feature extraction, and statistical analysis of incoming sensor data.
Machine learning models analyze patterns to detect anomalies and predict potential failures.
Generate maintenance work orders with prioritized recommendations based on risk and criticality.
Comprehensive sensor integration for complete equipment condition monitoring across all vessel systems.
Transition from reactive to predictive maintenance with sensor-driven intelligence and AI-powered analytics.
Predictive analytics identifies potential failures weeks before they occur.
Eliminate unnecessary preventive maintenance and optimize repair timing.
Early warning of equipment degradation prevents dangerous failures at sea.
Sensor integration, predictive analytics, and intelligent maintenance recommendations.
Live equipment health dashboards accessible from anywhere, anytime.
Connect existing sensors or deploy new IoT devices with minimal setup.
AI models improve accuracy over time as they learn from your fleet data.
Sensor Readings
Prediction Accuracy
Fewer Failures
Connected Sensors
See how our CBM platform is transforming maintenance operations with predictive intelligence.
"The vibration analysis detected a bearing defect 3 weeks before it would have failed. We scheduled the repair during port call and avoided a mid-voyage breakdown."
"Our maintenance costs dropped 35% in the first year. The system identifies exactly what needs attention, so we stopped doing unnecessary preventive maintenance."
"The real-time dashboard gives us complete visibility into equipment health across the entire fleet. We can make decisions based on data, not guesswork."
Comprehensive preventive-maintenance guidance for schedules and reports.
Templates and schedules for planned service intervals and maintenance windows.
View Service IntervalsInteractive dashboard for preventive schedule KPIs, upcoming tasks and asset health.
View DashboardReporting templates, export options and analytics for preventive maintenance activities.
View ReportsGet answers to common questions about our condition-based maintenance and sensor analytics platform.
MarineInspection supports a wide range of industrial sensors including vibration sensors (accelerometers, velocity transducers, proximity probes), temperature sensors (thermocouples, RTDs, infrared), pressure transmitters, flow meters, electrical monitors, and oil condition sensors. We integrate with major IoT gateway providers and support standard protocols like Modbus, MQTT, and OPC-UA.
Our AI engine uses machine learning algorithms trained on maritime equipment data to identify patterns associated with equipment degradation and failure. It analyzes sensor readings, detects anomalies, calculates remaining useful life (RUL), and generates failure probability scores. The models continuously improve as they learn from your fleet's specific equipment behavior.
Yes, our platform is designed to integrate with existing sensor infrastructure. We support data import from vessel monitoring systems, alarm management systems, and standalone sensors. Our team provides integration support to connect your current sensors to the CBM platform. We can also recommend and supply additional sensors if needed to fill monitoring gaps.
Our AI models achieve an average prediction accuracy of 92% for failure detection. Accuracy varies by equipment type and available sensor data - equipment with comprehensive monitoring typically sees higher accuracy. The system displays confidence scores for each prediction, allowing you to make informed decisions. Accuracy improves over time as models learn from your fleet's specific patterns.
The CBM platform supports both continuous connectivity and store-and-forward modes. With satellite or cellular connectivity, sensor data streams in real-time. In offline mode, data is stored locally and synchronized when connectivity is available. Critical alerts can be sent via satellite communication for immediate notification regardless of location.
Start monitoring equipment condition with sensor analytics and AI-powered predictions. Prevent costly failures and optimize maintenance timing with condition-based intelligence.