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Evaluation of Learning Behavior in Sensor and Testing Technology at Anhui Open University
Insights on Learning Sensor and Testing Technology (Anhui Open University)
Course Overview
The “Sensor and Testing Technology” course is a core course in the Electronic and Information Engineering program at Anhui Open University, aimed at cultivating students’ comprehensive abilities in sensor principles, classifications, applications, and testing system design and analysis. The course content covers the basic concepts of sensors, the working principles of typical sensors (such as temperature, pressure, displacement, optoelectronic, etc.), signal conditioning and conversion technologies, the design of Data Acquisition Systems (DAS), error analysis and data processing methods, as well as application cases of modern testing technologies in industrial automation, the Internet of Things, and other fields. Through a combination of theoretical learning and practical operations, the course helps students build a complete knowledge system from sensor selection to system integration.
Learning Methods and Process
1. Theoretical Learning: Strengthening the Foundation
– Textbook and Presentation Combination: The course textbook “Sensor and Detection Technology” (edited by Li Qingquan et al.) is systematic, but some content is abstract. I gradually understood the physical principles of sensors (such as piezoelectric effect, thermoelectric effect) and mathematical models (such as sensitivity, linearity, dynamic response) by repeatedly reading the textbook in conjunction with the online presentations provided by Anhui Open University.
– Key Difficulties Breakthrough: For challenging topics such as the dynamic characteristics analysis of sensors and signal conditioning circuit design, I utilized MOOC platforms (such as China University MOOC) to supplement relevant video courses and derived and verified concepts through experimental cases.
– Interdisciplinary Knowledge Integration: The course involves knowledge from multiple fields such as electronic circuits, signal processing, and mechanical design. I created mind maps to connect sensor types, application scenarios, and corresponding testing technologies, forming a knowledge network.
2. Practical Learning: Balancing Hands-On and Thinking
– Experimental Operations: The course includes 8 experiments, including calibration of temperature sensors (thermocouples, thermistors), static characteristic testing of pressure sensors, and signal acquisition from optical encoders. During the experiments, I focused on recording data and analyzing sources of error, such as discovering the importance of cold junction compensation in the thermocouple experiment.
– Project-Driven: Through the design project of an “Arduino-based Intelligent Temperature and Humidity Monitoring System,” I independently completed sensor selection, circuit connections, program writing, and system debugging, deeply realizing the gap between theory and practice, such as the significant impact of noise on signals in real environments compared to textbook cases.
– Online Discussions and Q&A: Utilizing Anhui Open University’s online learning platform, I discussed problems encountered in experiments (such as sensor non-linear calibration methods) with classmates and consulted with instructors multiple times to resolve challenges such as filter algorithm optimization and sensor drift compensation.
3. Key Difficulties Analysis
(1) Sensor Dynamic Characteristics Analysis
– Difficulty: The dynamic parameters of sensors, such as frequency response and phase delay, are difficult to understand intuitively, especially the correlation between step response and frequency response.
– Solution: By simulating the step input response curve of sensors using MATLAB, I observed the impact of time constants on output; compared the dynamic characteristics of different sensors (such as piezoelectric sensors and strain gauges) and verified theoretical models with experimental data.
(2) Signal Conditioning and Anti-Interference Technology
– Difficulty: In actual testing, factors such as electromagnetic interference and temperature drift can lead to signal distortion, making it crucial to design effective filtering circuits and compensation schemes.
– Solution: After learning the principles of low-pass filter design, I attempted to incorporate RC filtering circuits into experiments and further optimized signals through software filtering (such as moving average method); simultaneously, I mastered hardware and software implementation methods for temperature compensation through literature review.
4. Course Gains and Insights
(1) Knowledge System Construction
– Mastered the classification standards of sensors (by measured physical quantity, working principle, output signal type) and selection principles, such as choosing strain gauges or piezoelectric sensors based on accuracy requirements in pressure measurement.
– Understood the complete process of testing systems: from sensor signal acquisition, signal conditioning, data acquisition to data analysis, where optimization at each stage is crucial to the final result.
(2) Improvement of Engineering Practice Ability
– In the “Intelligent Temperature and Humidity Monitoring System” project, I independently completed the entire development process from hardware circuit design to software programming for the first time, learned to use Multisim for circuit simulation, and resolved issues of unstable sensor output through practical debugging.
– Through experiments comparing the performance indicators of different sensors (such as accuracy, response speed, cost), I recognized that in engineering practice, it is necessary to weigh technical indicators based on specific needs rather than blindly pursuing high accuracy.
(3) Change in Thinking Patterns
– The course cultivated my systematic engineering thinking; for example, when designing testing systems, I actively consider sensor installation methods, signal transmission paths, and environmental interference factors, rather than focusing solely on the sensor itself.
– Learned the concept of “testing is verification”: any theoretical model or design scheme needs to be validated and corrected through experimental data, such as discovering deviations between theoretical frequency response calculations and actual tests in designing vibration monitoring systems, requiring compensation through filter parameter adjustments.
5. Typical Case Analysis
Case: Design of Industrial Pipeline Pressure Monitoring System
– Requirement Analysis: A factory needs to monitor pressure changes in high-temperature and high-pressure pipelines in real-time, requiring sensors to withstand high temperatures (150°C), resist electromagnetic interference, and have an accuracy of ±0.5% FS.
– Selection and Design: Chose a high-temperature strain gauge pressure sensor (with built-in temperature compensation circuit), paired with an isolation amplifier and digital filtering algorithm, transmitting signals through a 4-20mA current loop to reduce interference.
– Experimental Verification: Built a simulation system in the laboratory, discovering that the sensor still exhibited a 0.3% drift at high temperatures, ultimately controlling the error within acceptable limits by adding a temperature sensor and introducing a software compensation algorithm.
6. Suggestions for the Course
1. Increase Industry Cases: It is suggested to supplement more actual fault cases from industrial sites (such as analysis of sensor failure reasons) to help students understand the application of theory in complex environments.
2. Strengthen Software Tool Training: Although the course involves MATLAB and LabVIEW, the practical time is limited; it is recommended to add specialized training modules for related software.
3. Optimize Experimental Equipment: Some experimental equipment (such as data acquisition cards) have limited performance, making high-frequency signal testing difficult; it is hoped that future upgrades to experimental equipment will align with industrial standards.
7. Future Learning Directions
(1) In-Depth Directions
– Intelligent Sensor Technology: In conjunction with MEMS sensors mentioned in the course, I plan to study embedded systems and machine learning algorithms to explore intelligent processing of sensor data.
– Internet of Things Applications: Combining sensors with wireless communication modules (such as LoRa, NB-IoT) to design remote monitoring systems, enhancing my understanding of IoT architecture.
(2) Career Development Connection
– As a working engineering technician, this course has helped me resolve issues such as sensor selection errors and unreasonable signal processing schemes in my work; I plan to apply what I have learned to factory automation transformation projects in the future.
– Considering obtaining certifications such as “Sensor Engineer” or “Industrial IoT Engineer” to further enhance my professional competitiveness.
Conclusion
The “Sensor and Testing Technology” course is not only a technical course but also a training in systematic engineering thinking. Through the combination of theoretical learning and practical application, I deeply appreciate the importance of sensors as the “source of information” and the core position of testing technology in Industry 4.0 and intelligent manufacturing. The challenges encountered in the course (such as noise suppression and non-linear calibration) made me realize that solving engineering problems requires a rigorous scientific attitude and flexible innovative thinking. In the future, I will continue to deepen my understanding of sensor technology and attempt to integrate it with emerging technologies such as artificial intelligence and big data to provide more possibilities for solving practical engineering problems.
Note: This article is based on the course content of Anhui Open University for the Fall 2023 semester, combined with personal practical projects and industry research materials, striving to reflect the integration of theory and practice. I would like to especially thank the instructors for their guidance on experimental design and the interactive support from the online discussion group.
Keywords: sensor principles, dynamic characteristics, signal conditioning, error analysis, IoT applications, engineering practice, system design