Ciencias Exactas y Ciencias de la Salud

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Machine learning model for road asphalt monitoring system: vibration-based approach
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12) López Castañeda, Carlos Alonzo; ZAREEI, MAHDI; 822705; Zareei, Mahdi; puemcuervo, emipsanchez; Roshan Biswal, Rajesh; Meza Ruiz, Ivan Vladimir; School of Engineering and Sciences; Campus Monterrey
To achieve safe and correct driving, it is necessary to have a surveillance plan and the maintenance of highways and roads, in order to maintain a good infrastructure. Mexico has a paved and unpaved network of 780, 511 km, of which is paved 174, 779 km. According to statistics from the INEGI, in 2019, there were 9,318 accidents due to poor road conditions. There are several types of breakdowns on any paved surface, and they may differ depending on the country. For example, potholes, cracks, and patches are some road surface damages essential to assess in Mexico. In 2020, INEGI presents that 96.8\% of the population identified the issue of potholes in streets and avenues, as the problem with the highest frequency nationwide, above crime. Thus, the conditions of our roads are of deep concern for the population. Different forms of road condition monitoring are proposed in the last years by specially designed instruments, using cameras, lasers, which require time and money and can only cover a limited proportion of the road network. Analogous to a video feed visually inspecting the asphalt's surface, a vibration-based system measures the ground conditions based on mechanical feedback from a vehicle. Different road anomalies, including potholes, cracks and ruts in the surface, create forces on the car, the frequency and magnitude of the forces will depend a lot on the type of anomaly. After we investigated different related works, this thesis is going to build on some of their aspects, and make a mix of others. The idea of dividing into three different categories for the classification of the roads, and the usage of supervised learning for road surface quality and anomaly detection. Regarding data collection, it was done through a phone with an Android system and an application created specifically for this job. This thesis proposes a pothole detection model using a vibration base method, using built-in vibration sensors in smartphones. We collected road condition data in Mexico City using a dedicated vehicle and smartphones with a purpose-built mobile application designed for this study, splitting the data into: bump, bump, normal. A processing method was applied to the collected data, and features were extracted, then classified with a neural network. The results indicated that using only the subset of two of the three selected event types, together with their six characteristics, they outperformed other subsets in identifying potholes. Our neural network classifier showed classification performance, with an accuracy of 98\%.
Road load data acquisition system with SAE-J1939 communications network: integration and laboratory test
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2018-05-14) Orellana Cruz, Oscar; Rodríguez González, Ciro Ángel; Siller Carrillo, Héctor Rafael; Martínez Romero, Oscar; Guedea Elizalde, Federico
This thesis discloses the results of a reliability analysis (R&R Study) through comparative method to validate a data acquisition (DAQ) system developed and built as a prototype. The laboratory conditions were established in order to test and validate the prototype when it acquires signals from accelerometers and strain gages as well as parameters taken from the electronic control unit (ECU), in this case a truck. The prototype equipment is composed of 9030 Compact RIO system with NI 9862 module for Controller Area Network (CAN) SAE J1939 and NI 9206 for analog inputs. 800 Hz sampling rate is programmed with LabVIEW code to acquire, store and analyze information. For the truck parameters, the code developed by Armando Ramírez in his research [6] was replicated and integrated into the code developed for the acquisition of signals with a user-friendly and versatile interface. The parameters are accelerator pedal position, engine speed, engine coolant temperature and wheel-based vehicle speed, with these parameters is possible to analyze the driving mode during the road tests. Instrumentation for acceleration was developed on a shaker to acquire the data, the frequency and wave amplitude were controlled by the use of a signal generator and signal amplifier. The reference data is acquired by a Brüel & Kjaer (B&K) module model 3160-A pattern equipment with PULSE Time Data Recorder software. Instrumentation for strain measurements was developed by simulating the strain gage measurement using a variable precision resistor. The reference data is acquired by a B & K module model 3160-A pattern equipment with PULSE Time Data Recorder software and two multimeters: OTC 55 series and MUL-280. The analysis range for these measurements is 0 to 80 Hz. The selected equipment demonstrated the DAQ system capability to perform vibration and deformation measurements with a resolution of 0.1 g and 100 μɛ respectively in the frequency range from 0 to 80 Hz, as well as obtain parameters from CAN J1939 protocol at the same time.

Ciencias Exactas y Ciencias de la Salud

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