Syndrome Diagnostics

Failure Detection and Isolation

Causation-Based Intelligence for Mission-Critical Systems

Syndrome Diagnostics (SD) revolutionizes Failure Detection and Isolation (FDI) for complex engineering systems. Built on the foundation of PHM Technology’s MADE Digital Risk Twin, SD delivers a new paradigm in diagnostics: Causation-based AI (Cb-AI) that not only detects failures but understands their origins and the engineering context of the failure.

How Syndrome Diagnostics Works

See how SD connects the dots between symptoms and root causes fast, accurate, and automated. SD integrates with MADE using the failure syndromes to identify every possible failure of a system. These Causal Relationships form the basis for the Cb-AI feature that is unique to SD.

Failure & Engineering Context in One Screen

There are many advantages that position SD and its approach using Cb-AI as the market leader. One of the most transformative is its ability to deliver Context-Rich Diagnostics that close the gap between symptoms and root cause.

Beyond Detection

How SD Redefines Failure Detection and Isolation and System Health Management

Causation-based AI

Using MADE’s DRT allows leveraging of causal relationships to gain a holistic view of the system to determine root causes of failure.

Engineering Content Integration

Delivers actionable insights with design-aware diagnostics.

Minimal Data Requirement

Achieve high prediction accuracy with limited historical data or sensor input.

Early Incipient Failure Detection

Prevent costly downtime with proactive maintenance.

Traceable Failure Paths

Full visibility into the fault propagation from source to effect.

Offline Inference Capability

Validate predictions locally before deployment.

Model-Based Thresholding

Combines domain knowledge with AI for accurate alerts.

Digital Twin Integration

Seamlessly links diagnostics with system RAMS design models.

Adaptability to System Changes

Maintain diagnostic performance without expensive retraining.

Smarter FDI. Deeper Confidence

Explore What’s Possible – Download the SD Brochure

Click the image to learn how Syndrome Diagnostics empowers engineering teams to make faster, more informed diagnostic decisions with model-backed logic.

Why Traditional FDI Tools Fall Short

Conventional FDI methods are limited, they often require extensive historical data, are prone to spurious correlation and overfitting, and fail to utilize engineering context. These limitations can delay detection, inflate costs, and compromise safety.

Syndrome Diagnostics changes this with:

Leverage causal relationships to go beyond symptoms to root causes. Unlike traditional black-box models, SD's Cb-AI maps the causal pathways linking faults, conditions, and observable effects, offering explainable diagnostics.

SD embeds operational logic and system behavior to provide reasoning paths, clarifying root mechanisms behind anomalies. This gives unparralled insight into how and why failures happen, not just that they did.

SD can deliver accurate results even with sparse or incomplete datasets by leveraging structured failure syndromes and design-intent models. This means you can utilize limited sensor data more effectively, thanks to embedded domain knowledge.

Cb-AI enables high integrity predictive diagnostics using condition evolution, shortening response times and boosting mission readiness. This means you can identify faults early, even before symptoms escalate, minimizing downtime and risk.

Exploratory Diagnostic Analysis Tool (EDA)

Evaluate, Clean, and Optimize Input Data to Power Syndrome Diagnostics

The Exploratory Diagnostic Analysis tool is purpose-built to help engineers assess and prepare the data that drives Syndrome Diagnostics. Before any algorithm is trained or deployed, the EDA tool allows you to interrogate the quality, consistency, and patterns within your failure data, identifying gaps, anomalies, and key correlations. With intuitive visualizations and smart filters, users can quickly determine if the intended input data is suitable for training, refine it where needed, and ensure that the diagnostic model built with SD is accurate, robust, and meaningful. It’s the critical first step in creating high confidence diagnostic intelligence.

Failure Dependency Mapping Visualization

Feature	Syndrome Diagnostics (Causation-Based)	Traditional FDI (Correlation-Only)
Failure Detection Method	Identifies root cause via causal paths from component behavior	Detects anomalies based on statistical correlations
Engineering Context Integration	Deep integration with system models and design context	Lacks engineering model awareness
Data Requirement	Performs reliably with minimal and incomplete data	Requires large historical datasets to train effectively
Failure Explanation	Explains how and why a fault occurs	Flags anomalies without detailed causal explanation
Early Fault Detection	Detects incipient and cascading failures early	Delayed detection due to reliance on correlated symptoms
Adaptability to System Changes	Robust to design updates via Digital Risk Twin integration	Requires retraining for new configurations
Maintenance Insights	Suggests targeted tasks and optimized strategies	Offers limited guidance beyond fault alert
Overfitting Risk	Low: grounded in deterministic failure logic	High: prone to overfitting if data is unbalanced or sparse
Spurious correlation	Knowledge of causal links avoids the need to correlate relationships	Relationships are established via correlation, leading to spurious results
Traceability	Full traceability from symptoms to root cause in system hierarchy	Poor traceability – black-box model outputs
Regulatory Alignment	Supports safety-critical standards with transparent logic paths	Often unsuitable for high-integrity environments