Causal Sensor DAG Modeling for Anomaly Root Cause Back-Tracking
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Solution Overview
Problem
High-dimensional sensor data from IoT devices in industrial applications poses challenges in manual root cause analysis due to its dimensionality and size, making it expensive and inefficient to identify causal relationships and anomaly events in real-time.
Innovation Solution
A system that preprocesses sensor data by aligning it with a unified global reference time, applying data summarization and interpolation techniques, and builds an optimal Directed Acyclic Graph (DAG) to model causal dependencies, allowing for automated root cause analysis of anomaly events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of sensor data is performed, then causal relationships can be identified with human expertise, but the process becomes prohibitively expensive and inefficient as data dimensionality grows
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computational system that uses directed acyclic graphs (DAGs) and probabilistic models to identify causal relationships. The system automatically processes sensor data, builds causal models, and traces anomaly origins without human intervention, thereby maintaining analytical accuracy while dramatically improving efficiency and scalability.
Solution Approach 2:
The patent introduces a DAG-based causal model as an intermediary structure between raw sensor data and root cause identification. This intermediate representation captures causal relationships in a structured format, enabling automated reasoning and efficient anomaly tracing while preserving the analytical depth previously requiring manual expertise.
2Adaptability or versatility
If the dimensionality and size of sensor data increase, then more comprehensive monitoring coverage is achieved, but manual study and identification of causal relationships becomes prohibitively expensive
Solution Approach 1:
The patent segments the complex high-dimensional sensor data into structured causal relationships represented by nodes and edges in a DAG. Each sensor becomes a node, and causal influences become directed edges, breaking down the overwhelming data complexity into manageable, interpretable units that can be processed automatically at scale.
Solution Approach 2:
The patent transforms raw sensor readings into probabilistic state representations within the DAG framework. By changing the parameter representation from raw values to causal probability states, the system can handle high-dimensional data efficiently while maintaining the ability to identify root causes through probabilistic reasoning along causal pathways.
3Productivity
If automated root cause analysis is implemented, then scalability to real-world applications is improved, but the system must handle noisy and corrupted sensor data
Solution Approach 1:
The patent incorporates robust probabilistic modeling and data preprocessing techniques that anticipate and cushion against the effects of noisy and corrupted data. The DAG framework and probability-based causal inference are designed to tolerate data quality issues, maintaining reliable root cause identification even when sensor readings are imperfect or missing.
Data Source
AI summary
One embodiment of the present invention can provide a system for identifying a root cause of an anomaly event in operation of one or more machines is provided. During operation, the system can obtain sensor data from a set of sensors associated with the one or more machines, convert the sensor data into a set of sensor states, build an optimal DAG based on the set of sensor states to model causal dependency; determining, by using the DAG, a probability of an anomaly state of a target sensor given a state of a direct neighbor sensor, and determining a root cause of the anomaly event associated with the target sensor by back-tracking the anomaly state in the DAG.