Motor Jam Detection Using Recursive Filters and Back-EMF
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Solution Overview
Problem
Existing motor error detection systems in battery-powered appliances like blenders fail to accurately detect jams or overloads due to dynamic motor drive conditions, leading to delayed detection or false negatives, especially in systems with reduced torque, and often require long timeout periods that treat normal running conditions as jams, causing unnecessary shutdowns.
Innovation Solution
An apparatus and method using discrete-time recursive filters to model expected motor behavior, compare it with actual responses, and identify motor errors by iteratively revising the model with filter inputs, allowing for dynamic threshold adjustments and early detection of jams through continuous monitoring of motor current and back-EMF.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed threshold timeout method is used for jam detection, then the system can detect overload conditions, but it treats normal running conditions as jams during dynamic motor operation, causing delayed detection and unnecessary shutdowns
Solution Approach 1:
The patent applies dynamics by replacing the static fixed threshold timeout method with a dynamic filter-based approach. The filter continuously adapts to the motor's actual operating conditions, adjusting the expected current range in real-time based on drive voltage, frequency, and load variations. This allows the system to distinguish between normal dynamic current changes and actual jam conditions, eliminating delayed detection and false shutdowns while maintaining reliable jam detection.
2Reliability
If a long timeout period is used to account for all motor error situations, then the system avoids false positives, but it significantly delays jam detection during normal running conditions
Solution Approach 1:
The patent uses dynamics to create an adaptive detection system that adjusts its sensitivity based on real-time motor operating conditions. The filter continuously updates the expected current range according to drive voltage, frequency, and load, allowing the system to maintain high reliability (low false positive rate) while enabling rapid detection during actual jams. This eliminates the need for long timeout periods and preserves operational continuity.
Solution Approach 2:
The patent implements feedback by continuously monitoring motor current and comparing it against the dynamically updated filter model. The system uses this feedback to distinguish between normal operating variations and actual jam conditions, maintaining high reliability without requiring long timeout periods. The feedback loop enables rapid response to genuine jams while avoiding false positives during normal operation.
3Device complexity
If simple current threshold monitoring is used, then the device complexity is low, but the system cannot accurately distinguish between normal dynamic behavior and jam conditions in battery-powered motors with reduced torque
Solution Approach 1:
The patent applies copying by creating a virtual model (filter) that replicates the motor's expected current behavior under various operating conditions. Instead of using complex physical sensors or multiple monitoring points, the system uses a software-based filter model that copies and simulates normal motor operation patterns. This allows accurate distinction between normal dynamic behavior and jam conditions while keeping the physical detection system simple and cost-effective.
Data Source
AI summary
An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to: model a response of a motor to a drive signal using one or more filters, the one or more filters configured to define a range of expected motor behaviour associated with the drive signal; compare the modelled response with an actual response of the motor to the drive signal; and identify a motor error if the actual response of the motor is outside the range of expected motor behaviour defined by the modelled response.