Comfort System Anomaly Detection Using Multi-Level Profile Comparison
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Solution Overview
Problem
Current comfort systems, such as ventilation and HVAC systems, face inefficiencies and anomalies due to incorrect installations, blockages, leaks, and sensor failures, requiring time-consuming and costly on-site technician interventions for diagnosis and repair, which are not scalable or predictive.
Innovation Solution
A device and method for anomaly detection in comfort systems that collects and analyzes sensor, operational, and external data to generate profiles for each apparatus type and individual apparatus, comparing recent data to standard profiles to detect deviations and anomalies, enabling continuous, automatic detection without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional on-site technician intervention is used for anomaly detection and diagnosis, then accurate detection can be achieved through expert inspection, but the process is time-consuming, labour-intensive, expensive, and not scalable
Solution Approach 1:
The comfort system performs self-diagnosis by automatically collecting sensor data, operational data, and external data, generating profiles, comparing data against profiles, and detecting anomalies without requiring technician intervention. The system serves itself by implementing the full anomaly detection workflow autonomously.
Solution Approach 2:
The patent replaces the mechanical/physical inspection process performed by technicians with an automated electronic system that collects data through interfaces, processes it through profile modules and analyzing modules, and detects anomalies algorithmically. This substitutes human expertise with automated computational analysis.
2Reliability
If traditional on-site technician intervention is used for anomaly detection, then accurate diagnosis can be achieved, but the approach is not scalable and depends on technician availability
Solution Approach 1:
The system autonomously performs data collection, profile generation, data comparison, and anomaly detection without requiring external technician intervention. This self-service capability enables the system to handle multiple anomalies simultaneously across different locations, significantly improving scalability while maintaining consistent diagnosis quality through standardized automated processes.
Solution Approach 2:
The anomaly detection device is designed as a universal system that can detect various types of anomalies in different comfort system apparatuses (ventilation motors, valves, flow regulators, sensors) using the same automated workflow. The system handles multiple functions including data collection from various sources, profile generation for different apparatus types, and anomaly detection across the entire comfort system.
3Productivity
If automated anomaly detection is implemented, then scalability and cost-effectiveness improve, but the system complexity increases due to multiple data collection interfaces and profile modules
Solution Approach 1:
The anomaly detection device is divided into distinct functional modules: data collection interfaces for sensor/operational/external data, a database module for storage, profile modules for generating apparatus-type and apparatus-specific profiles, an analyzing module for comparing data against profiles, and an anomaly detection module for identifying deviations. This segmentation manages complexity by organizing functions into separate, manageable components.
Solution Approach 2:
The patent introduces a database as an intermediary layer between data collection and analysis functions. The database stores sensor data, operational data, and external data as time sequences, serving as a central repository that decouples data collection from data processing. Profile modules and analyzing modules interact with the database rather than directly with data sources, simplifying the overall system architecture.
4Reliability
If frequent technician visits are scheduled for preventive maintenance, then system reliability can be maintained, but operational downtime and costs increase
Solution Approach 1:
The automated anomaly detection system continuously monitors comfort system apparatuses and detects potential failures before they occur by comparing real-time data against established profiles. This preliminary detection enables preventive maintenance to be scheduled only when actually needed, rather than following fixed maintenance schedules, thereby maintaining system reliability while minimizing operational downtime and maintenance costs.
Data Source
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AI summary
An anomaly detection device (100) for detecting an anomaly in a comfort system (150), comprising: - interfaces (101, 102, 103) to obtain sensor data and operational data from the comfort system (150) and external data from sources (153) outside the comfort system (150); - a central database (105) to store these data; - a first profile module (106) to generate an apparatus-type profile (108) for each type of apparatus or sensor; - a second profile module (107) to generate an apparatus-profile (109) for each individual apparatus or sensor; - an analysing module (110) configured to: - in a first test (111), compare recent data to an apparatus-type profile (108); - in a second test (112), compare recent data to an apparatus-profile (109); - in a third test (113), compare recent data to the apparatus-profile of one or more related apparatuses; - an anomaly detection module (120) to detect an anomaly on the basis of a deviation in the first test (111), the second test (112) and/or the third test (113) in excess of a threshold.