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Design and Development of a Retrofitting system for waste collection vehicles enabling predictive maintenance applications

Daniel Pablo Trigo Nagel

Design and Development of a Retrofitting system for waste collection vehicles enabling predictive maintenance applications.

Rel. Marco Vacca. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022

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The following project is focused on the design and implementation of a predictive maintenance system for a hydraulic system of a waste collection truck. This thesis is an outcome of the collaboration with the Competence Industry Manufacturing 4.0 of Torino, which at the same time collaborated for this project with the company SEA Ambiente, which is in charge of collecting the waste of different city locations. After taking the Failure Mode and Effects Analysis of the hydraulic system provided by the SEA company. Two main failures were identified, the first one is oil leakage in the hydraulic cylinders and the second wear in the rail pads where the hydraulic actuators perform a linear displacement for trash compaction. Using the detected failures as a starting point for the predictive maintenance procedure. A set of sensors was selected in order to help with the detection, analysis of failures in the system and further causalities which lead to oil leakage in the system cylinders. The selected sensors are the following: • An oil health monitoring sensor for contaminant particle detection. • A pair of ultrasonic distance measure sensors for detecting the level of oil in the reservoir for big leakage amounts. • A pair of pressure sensors for small leakage detection. • A temperature sensor for detecting internal fluid overheating in the system. • A pair of accelerometers for failure detection of the rail pads where the hydraulic actuators perform a linear displacement. • A couple of inductive sensors to detect the wear in the Teflon rail pads which help with the sliding operation of the hydraulic actuators in the rails. For the Data acquisition system an Arduino Mega 2560 and a Raspberry Pi 4 boards were used, the Arduino board processed the data of all the sensors except the accelerometers which were processed by the Raspberry Pi due to the high data rate, SERIAL communication was used with the boards and the sensors. The data acquisition system contained the folowing modules: • Battery module, which provides the power for the data acquisition system. • Binary signals module which correspond to the signals already implemented on the truck for the hydraulic system operation • Sensors Module which contains all the previous mentioned sensors. • 4G Router module which sends the collected sensor data via WiFi connection to a cloud • The SEA BOX module where was implemented the Arduino and Raspberry connection with the sensors . Also it was implemented a power distribution module to distribute the power to all the system components and a signal conditioning module to process the binary signals of the hydraulic system. • A GNSS module was also installed in order to send the car location in real time. Since the real system needed time to obtain failure data, a model based approach of the hydraulic system was also developed using Simscape, which operates in the Simulink environment, such dynamic model simulated normal operating conditions and failure conditions of the hydraulic system where internal and external leakages are modelled and simulated for data acquisition and later analysis. The pressure signal was used to detect small internal and external leakage of the hydraulic cylinder with signal processing techniques, two techniques were used, Wavelet transform and Hilbert Huang transform the leakage detection was done comparing RMS values of healthy and faulty conditions of the system

Relators: Marco Vacca
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 122
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: Competence Industry Manufacturing 4.0
URI: http://webthesis.biblio.polito.it/id/eprint/23555
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