Prediction system, prediction method, and program
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining drain pump contamination in air conditioners are prone to erroneous detection due to significant fluctuations in current or revolution values, leading to potential failures.
Innovation Solution
A prediction system that utilizes a controller to analyze changes in drain pump data over predetermined periods, using cumulative and moving averages, along with environmental data and machine learning models, to accurately predict anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If instantaneous current value or number of revolutions is used to determine drain pump contamination, then the detection method is simple, but erroneous detection occurs due to significant fluctuations in the values
Solution Approach 1:
The system performs preliminary actions by collecting drain pump data over a predetermined period before making an anomaly determination. Instead of immediately judging based on instantaneous values, the controller accumulates data points and calculates statistical metrics (average value and standard deviation) to establish a baseline for normal operation variations, thereby preventing erroneous detections
Solution Approach 2:
The system introduces feedback by continuously monitoring drain pump data, calculating statistical metrics, comparing against thresholds, and using the results to determine anomalies. The controller feeds back the determined anomaly status and continues monitoring, allowing dynamic adjustment and reducing false positives through continuous evaluation rather than single-point assessment
2Measurement precision
If statistical analysis over predetermined period is used to predict drain pump anomaly, then prediction accuracy is improved, but the complexity of the system increases
Solution Approach 1:
The system applies self-service by utilizing the drain pump's own operational data (current value or number of revolutions) to predict its own anomalies. The controller processes the drain pump's intrinsic data through statistical analysis, eliminating the need for external complex diagnostic equipment or additional sensors, thereby improving accuracy without proportionally increasing system complexity
Solution Approach 2:
The system changes parameters by transforming instantaneous physical measurements (current value or revolutions) into statistical parameters (average value and standard deviation) over time. This parameter transformation allows the system to capture trends and variations that indicate anomalies while using computationally efficient statistical methods that don't require complex algorithms or additional hardware
Data Source
AI summary
In order to enable more accurate prediction of an anomaly of a drain pump provided in an air conditioner, a prediction system includes an air conditioner including a drain pump and a controller, and the controller acquires data of a number of revolutions of the drain pump or a current value of the drain pump, and outputs a prediction result indicating that an anomaly of the drain pump is predicted, based on a change in data for a predetermined period in the data.