Domestic Appliance Operation State Determination via Time Series Model Comparison
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Solution Overview
Problem
Existing systems for monitoring the operation of heating systems, such as boilers, trigger alerts instantaneously for each parameter, making it difficult to analyze the operation or predict faults until the threshold or rate of change is reached, leading to inefficient and costly maintenance.
Innovation Solution
A method that monitors the operation of heating systems over a cycle, comparing received data with models to determine the state of operation, allowing for in-depth analysis and timely preemptive maintenance by considering multiple parameters and trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If instantaneous alert triggering is used for each monitored parameter, then the system responds quickly to faults, but in-depth analysis of system operation and fault cause becomes difficult
Solution Approach 1:
The patent combines multiple monitored parameters and their temporal relationships into a unified analysis model. Instead of treating each parameter independently with separate thresholds, the system integrates parameters into a holistic operational model that preserves contextual relationships, enabling both quick response and deep analysis simultaneously.
Solution Approach 2:
The system performs preliminary analysis by comparing current parameter values against pre-established operational models and historical patterns. This allows the system to quickly determine whether a parameter deviation represents a genuine fault or normal variation, maintaining fast response while preserving analytical depth through pre-computed reference frameworks.
2Reliability
If triggering thresholds are set low to detect early faults, then pre-emptive maintenance can be planned, but false alarms increase leading to unnecessary maintenance
Solution Approach 1:
The system incorporates feedback loops where alert generation is not based solely on static thresholds but on dynamic comparison with operational models and historical data. This feedback mechanism allows the system to learn from past operations and adjust its sensitivity, reducing false alarms while maintaining early fault detection capability through adaptive thresholding based on contextual patterns.
Solution Approach 2:
The patent transforms fixed triggering thresholds into dynamic, context-dependent parameters. Instead of using constant threshold values, the system adjusts sensitivity based on operational conditions, historical patterns, and parameter interrelationships. This allows early fault detection in critical scenarios while maintaining high thresholds during normal variations, optimizing both reliability and maintenance efficiency.
3Reliability
If multiple parameters are monitored independently with separate thresholds, then comprehensive coverage is achieved, but the complexity of analysis increases and trends are missed
Solution Approach 1:
The patent implements a universal operational model that serves multiple functions simultaneously: it monitors individual parameters, detects trends across parameters, provides contextual analysis, and generates alerts. This multi-functional framework eliminates the need for separate analysis mechanisms for each parameter, reducing overall system complexity while maintaining comprehensive monitoring coverage through a unified approach.
Data Source
AI summary
Determination of a state of operation of a domestic appliance In one embodiment it is provided a method for determining a state of operation of a domestic appliance (2) in a plurality of domestic appliances (2), having: receiving (S10), from the domestic appliance (2), a time series (51, 52, 53, 54) of data (5) relating to the operation of the domestic appliance (2) over a cycle (4, 7) of operation; and determining (S20) the state of operation of the domestic appliance (2) based on comparing the received time series (51, 52, 53, 54) with a model of time series (151, 152, 153, 154; 251, 252, 253, 254, 255; 351, 352, 353, 354, 355) of data (50) corresponding to the operation of the plurality of domestic appliances (2) over a cycle (4, 7) of operation.