Abstract: A refuse collection vehicle has various hydraulic actuators including at least one cylinder with an internal seal. A sensor is responsive to movement of the piston and sends a signal to a controller to indicate piston movement information during an associated vehicle body component movement. The controller is configured to determine a motion characteristic of the piston during a predetermined body component movement, compare the determined motion characteristic to a stored reference motion characteristic, and in response to determining that a difference between the determined motion characteristic and the reference motion characteristic is greater than a predetermined value, trigger an indication that the refuse collection vehicle is in need of service.

Abstract: A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.

Abstract: A refuse collection vehicle has various hydraulic actuators including at least one cylinder with an internal seal. A sensor is responsive to movement of the piston and sends a signal to a controller to indicate piston movement information during an associated vehicle body component movement. The controller is configured to determine a motion characteristic of the piston during a predetermined body component movement, compare the determined motion characteristic to a stored reference motion characteristic, and in response to determining that a difference between the determined motion characteristic and the reference motion characteristic is greater than a predetermined value, trigger an indication that the refuse collection vehicle is in need of service.

Abstract: A refuse collection vehicle has various hydraulic actuators including at least one cylinder with an internal seal. A sensor is responsive to movement of the piston and sends a signal to a controller to indicate piston movement information during an associated vehicle body component movement. The controller is configured to determine a motion characteristic of the piston during a predetermined body component movement, compare the determined motion characteristic to a stored reference motion characteristic, and in response to determining that a difference between the determined motion characteristic and the reference motion characteristic is greater than a predetermined value, trigger an indication that the refuse collection vehicle is in need of service.

Abstract: A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.

Abstract: A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.

Abstract: A refuse collection vehicle has various hydraulic actuators including at least one cylinder with an internal seal. A sensor is responsive to movement of the piston and sends a signal to a controller to indicate piston movement information during an associated vehicle body component movement. The controller is configured to determine a motion characteristic of the piston during a predetermined body component movement, compare the determined motion characteristic to a stored reference motion characteristic, and in response to determining that a difference between the determined motion characteristic and the reference motion characteristic is greater than a predetermined value, trigger an indication that the refuse collection vehicle is in need of service.

Abstract: A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.