Power tool stall detection
A power tool includes a housing, a motor supported by the housing, a battery pack interface configured to receive a battery pack, a plurality of sensors configured to generate sensor data indicative of an operational state of the power tool, and an electronic controller. The electronic controller includes an electronic processor and a memory. The memory includes a machine learning control program for execution by the electronic processor. The electronic controller is configured to receive the sensor data indicative of the operational state of the power tool, process the sensor data using the machine learning control program to determine whether the power tool is experiencing a stall condition, and disable the motor when the power tool is determined to be experiencing the stall condition.
1 . A power tool comprising:
a housing;
a motor supported by the housing;
an inverter;
a battery pack interface configured to receive a battery pack;
a plurality of sensors configured to generate sensor data indicative of an operational state of the power tool, the plurality of sensors including a temperature sensor configured to sense a temperature associated with the inverter; and
an electronic controller including an electronic processor and a memory, the memory including a machine learning control program for execution by the electronic processor, the electronic controller configured to:
receive the sensor data indicative of the operational state of the power tool,
process the sensor data using the machine learning control program to determine whether the power tool is experiencing a stall condition,
process the sensor data using the machine learning control program to distinguish between the stall condition and a cold startup operation, and
disable the motor when the power tool is determined to be experiencing the stall condition.
2 . The power tool of claim 1 , wherein the power tool is a fastener driver.
3 . The power tool of claim 2 , wherein the plurality of sensors includes a motion sensor.
4 . The power tool of claim 3 , wherein the motion sensor is selected from a group consisting of an accelerometer, a gyroscope, and an inertial measurement unit.
5 . The power tool of claim 1 , wherein the power tool is a sander.
6 . The power tool of claim 5 , wherein the plurality of sensors includes a motion sensor.
7 . A method of operating a power tool comprising:
generating, by a plurality of sensors, sensor data indicative of an operational state of the power tool, the plurality of sensors including a temperature sensor configured to sense a temperature associated with an inverter of the power tool;
receiving, by an electronic controller of the power tool, the sensor data indicative of the operational state of the power tool;
processing, by the electronic controller of the power tool, the sensor data using a machine learning control program to determine whether the power tool is experiencing a stall condition;
processing, by the electronic controller of the power tool, the sensor data using the machine learning control program to distinguish between the stall condition and a cold startup operation; and
disabling a motor of the power tool when the power tool is determined to be experiencing the stall condition.
8 . The method of claim 7 , wherein the power tool is a fastener driver.
9 . The method of claim 8 , wherein the plurality of sensors include a motion sensor.
10 . The method of claim 9 , wherein the motion sensor is selected from a group consisting of an accelerometer, a gyroscope, and an inertial measurement unit.
11 . The method of claim 7 , wherein the power tool is a sander.
12 . The method of claim 11 , wherein the plurality of sensors includes a motion sensor.
13 . A power tool comprising:
a housing;
a motor supported by the housing;
an inverter;
a battery pack interface configured to receive a battery pack;
a plurality of sensors configured to generate sensor data indicative of an operational state of the power tool, the plurality of sensors including a temperature sensor configured to sense a temperature associated with the inverter, a current sensor, a speed sensor, and a motion sensor; and
an electronic controller including an electronic processor and a memory, the memory including a machine learning control program for execution by the electronic processor, the electronic controller configured to:
receive the sensor data indicative of the operational state of the power tool,
process the sensor data using the machine learning control program to determine whether the motor is experiencing a stall condition,
process the sensor data using the machine learning control program to distinguish between the stall condition and a cold startup operation, and
disable the motor when the motor is determined to be experiencing the stall condition.
14 . The power tool of claim 13 , wherein the power tool is a fastener driver.
15 . The power tool of claim 14 , wherein the plurality of sensors include a voltage sensor.
16 . The power tool of claim 13 , wherein the power tool is a sander.
17 . The power tool of claim 13 , wherein the machine learning controller program implements one or more of a group consisting of a decision tree learning, an artificial neural network, a recurrent artificial neural network, a long short term memory neural network, a support vector machine, clustering, a Bayesian network, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, and k-nearest neighbor.