Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing
<p>Examples of development boards and open-source hardware applications: (<b>a</b>) Open Source Hardware and Open Source Initiative logos; (<b>b</b>) Arduino Uno; (<b>c</b>) Intel Edison development board; (<b>d</b>) Texas Instruments Launchpad; (<b>e</b>) STM32 Nucleon board; (<b>f</b>) photodynamic therapy device to detect hepatitis C; (<b>g</b>) portable laboratory platform for hepatitis C detection; and (<b>h</b>) system for measuring incident light in photovoltaic applications [<a href="#B16-inventions-09-00120" class="html-bibr">16</a>,<a href="#B17-inventions-09-00120" class="html-bibr">17</a>,<a href="#B18-inventions-09-00120" class="html-bibr">18</a>,<a href="#B19-inventions-09-00120" class="html-bibr">19</a>,<a href="#B20-inventions-09-00120" class="html-bibr">20</a>,<a href="#B21-inventions-09-00120" class="html-bibr">21</a>,<a href="#B22-inventions-09-00120" class="html-bibr">22</a>,<a href="#B23-inventions-09-00120" class="html-bibr">23</a>,<a href="#B24-inventions-09-00120" class="html-bibr">24</a>].</p> "> Figure 1 Cont.
<p>Examples of development boards and open-source hardware applications: (<b>a</b>) Open Source Hardware and Open Source Initiative logos; (<b>b</b>) Arduino Uno; (<b>c</b>) Intel Edison development board; (<b>d</b>) Texas Instruments Launchpad; (<b>e</b>) STM32 Nucleon board; (<b>f</b>) photodynamic therapy device to detect hepatitis C; (<b>g</b>) portable laboratory platform for hepatitis C detection; and (<b>h</b>) system for measuring incident light in photovoltaic applications [<a href="#B16-inventions-09-00120" class="html-bibr">16</a>,<a href="#B17-inventions-09-00120" class="html-bibr">17</a>,<a href="#B18-inventions-09-00120" class="html-bibr">18</a>,<a href="#B19-inventions-09-00120" class="html-bibr">19</a>,<a href="#B20-inventions-09-00120" class="html-bibr">20</a>,<a href="#B21-inventions-09-00120" class="html-bibr">21</a>,<a href="#B22-inventions-09-00120" class="html-bibr">22</a>,<a href="#B23-inventions-09-00120" class="html-bibr">23</a>,<a href="#B24-inventions-09-00120" class="html-bibr">24</a>].</p> "> Figure 2
<p>Block diagram of the electronic circuit components and connections.</p> "> Figure 3
<p>Perfboard with the daughter boards attached.</p> "> Figure 4
<p>Mainboard and peripheral boards.</p> "> Figure 5
<p>External and internal structures of the PoC device.</p> "> Figure 6
<p>Overview of the structural components and parts of the PoC.</p> "> Figure 7
<p>Block diagram of the code behavior.</p> "> Figure 8
<p>Overview of the structural test setup.</p> "> Figure 9
<p>Sound levels of the motor (blue), motor and load (red), and motor and generator (yellow).</p> "> Figure 10
<p>Overview of the vibration dispersion over time between motor, load, and generator in (<b>a</b>) x-axis and (<b>b</b>) y-axis.</p> "> Figure 11
<p>Overview of the temperature difference between motor, generator, and load.</p> "> Figure 12
<p>The FFT response from the accelerometer.</p> "> Figure 13
<p>The FFT response from the microphone.</p> ">
Abstract
:1. Introduction
- Development of an Open-Source Data Logger System: This paper describes the design and implementation of an open-source data logger system for monitoring and fault detection in bench testing of internal combustion engines and electrical engines. This system is equipped to collect and analyze data on vibration, sound, temperature, and CO2 levels (combustion engines), which are crucial for identifying ignition and combustion faults;
- Proof of Concept (PoC) Validation: The study presents a comprehensive proof of concept (PoC) that validates the functionality and applicability of the data logger system. The PoC demonstrates the system’s capability to efficiently acquire data and create datasets for research and development purposes;
- Integration of Non-Invasive Sensors: Implementing non-invasive sensors, such as accelerometers, microphones, thermocouples, and gas sensors, ensures minimal interference with engine operation while providing accurate real-time data for fault detection and monitoring;
- Cost-Effective Solution: By leveraging open-source technology and low-cost components, the developed system offers a cost-effective solution for fault detection and monitoring, making it accessible for a wide range of applications in both research and practical scenarios;
- Future Research Directions: This paper identifies potential future improvements, including the enhancement of Wi-Fi and cloud capabilities for remote telemetry, the creation of comprehensive datasets, and the development of advanced diagnostic methodologies using the collected data. These directions aim to foster innovation and improve the reliability and sustainability of internal combustion engines.
- Education and training: The system can be implemented as a practical tool in engineering courses. Its affordable cost and functional design make it ideal for teaching concepts such as signal analysis and engine diagnostics and fostering innovation and experimentation in academic environments.
- Naval and transport sector: In this sector, marine engine telemetry enables remote real-time analysis. This functionality enhances operational efficiency, reduces greenhouse gas emissions, and improves reliability in transport operations, promoting a more sustainable approach.
- Industrial platforms: The system can be used for the continuous monitoring of engines, focusing on detecting abnormal vibrations and excessive temperatures. This application is critical for preventing catastrophic failures, extending equipment lifespan, and reducing unplanned operational interruptions.
- Renewable energy sector: The system can be applied to diagnose engines in wind turbines or hybrid generation systems. This usage increases energy efficiency, improves integration with IoT systems, and reduces environmental impacts, fostering more sustainable solutions.
2. Open-Source Hardware Applications
3. Materials and Methods
3.1. PoC Design and Specifications
- Temperature Sensor: Detects operational anomalies in diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These measurements are critical for identifying failures in the cooling system, thermostat valves, or potential coolant leaks.
- Acoustic Sensor: Identifies abnormal noises that may indicate issues, such as misaligned belts, valve knocking, timing irregularities, or loose components. This sensor helps diagnose mechanical inconsistencies that can compromise engine performance.
- Vibration Sensor: Detects displacement-related issues, such as failures in engine mounts, cracks in the engine block, or defects in pistons and valves. When combined with acoustic sensors, it provides a more comprehensive fault detection mechanism.
- Organic Compound and CO2 Sensors: Monitor fuel combustion efficiency and detect failures in the exhaust system, ensuring proper burning performance and reducing emissions.
- Temperature Sensor: Monitors operational anomalies, including overload conditions, bearing problems, or excessive shaft loads that can lead to mechanical failure.
- Acoustic Sensor: Diagnoses coil irregularities, phase imbalances, bearing defects, or faults in chain and belt systems, ensuring smooth motor operation.
- Vibration Sensor: Detects issues such as shaft misalignments, bearing wear, insufficient motor mounting, or overload conditions, helping to prevent motor inefficiencies and failures.
3.2. PoC Hardware and Assembly
3.3. PoC Programming Code
4. Results and Discussion
5. Measurements and Analysis
6. Conclusions and Future Perspectives
- Dataset Creation: A critical step involves collecting telemetry data from various combustion and electric engines, potentially introducing intentional faults to create a dedicated fault dataset. This approach aims to enable more precise diagnostics and predictive fault identification.
- TensorFlow Lite Micro Implementation: TensorFlow Lite Micro will be deployed on the ESP32 microcontroller, enabling machine learning models to detect subtle anomalies that might go unnoticed by humans. Once trained, these AI models will refine fault detection accuracy.
- TensorFlow Lite Micro Implementation: Periodic cloud backups will be established to ensure secure data storage. This enhancement will enable remote data access and add an extra layer of fault tolerance.
- Raspberry Pi Piggyback: A supplementary server module will be developed using Raspberry Pi 5. The PoC will transmit data via Wi-Fi to this module, which will manage storage, perform cloud backups, and execute advanced AI models using the full version of TensorFlow.
- Cloud Backup Implementation: Periodic cloud backups will be established, allowing data to be sent to an online server as a secure storage solution.
- Human–Machine Interface (HMI): The current interface will be enhanced with a mobile application and an HTTP-based interface, allowing users to monitor the system’s performance and progress through cloud services.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Ref. | Application | Equipment/Sensors | Type of Fault |
---|---|---|---|
[7] | CAT G3516B engine—16 cylinders: Tektronix TDS 420 A oscilloscope; magnetic pick-up; Kistler Type 6055BB pressure sensor; and PCB Piezotronics 353B17 accelerometers. | Combustion layer failures, ignition failures, elastic sealing, and mechanical failures. | |
[8] | Engine from Ford Focus. | IC engine from Ford Focus: chassis dynamometer and Vibration PCB Piezotronics ICP. | Exhaust valve damage, injector damage, and cylinder head gasket damage. |
[9] | A four-stroke RUGGERINI RY125 diesel engine; Sulzer 6AL20/24 marine engine; and Woodward PGA-type multi-scope rotational speed controller. | Reliability status. | |
[10] | ICP 338B34 single-axis piezoelectric accelerometers and a 482A16 PCB® Piezotronics INC amplifier. | IC engine valve clearance diagnosis. | |
[11] | A single-axis PCB 352C03 ceramic shear ICP accelerometer. | Control valve failure. | |
[12] | Kubota D905 engine | Kubota D905 3-cylinder engine: Monitran MTN/1100SC constant current accelerometers. | Mechanical failures: big end bearing wear and cylinder leakage. |
[13] | Integrated hardware and software platform: Arduino Due and CMA-4544PF-W electret condenser microphone and processing software running on smartphones. | Engine misfires and alternator belt problems. |
Ref. | Technique | Sensors | Type of Fault |
---|---|---|---|
[7] | Discrete Fourier Transform (DFT) | Vibration and acoustic pressure | Combustion layer failures and ignition failures. |
[8] | Discrete Wavelet Transform (DWT) and Probabilistic Neural Networks (PNNs) | Vibration and acoustic | Exhaust valve damage, injector damage, and cylinder head gasket damage. |
[9] | Support Vector Machine (SVM) | Sensor array | Reliability status. |
[10] | Artificial Neural Network Multi-layer Perceptrons (MLPs) | Vibration | Valve clearance classification. |
[11] | Fast Fourier Transform (FFT) and Support Vector Machine (SVM) | Vibration | Control valve failures. |
[12] | Parzen Windows Density Estimation + One-Class Support Vector Machine (OCSVM) | Vibration and pressure | Mechanical failures: big end bearing wear and cylinder leakage. |
[13] | Artificial Neural Network + Discrete Wavelet Transform (DWT) + Fractal Dimension | Vibration and chaos analysis | Engine misfires and alternator belt problems. |
[14] | Artificial Neural Network Multi-layer Perceptrons (MLPs) and Probabilistic Neural Networks (PNN) | Vibration and temperature | Combustion and mechanical failures. |
[15] | Wavelet Multiresolution Analysis (WMA) and Chaos using maximum density (SAC-DM) | Accelerometer | Misfires. |
PART | MODEL | PRICE |
---|---|---|
ESP32 | ESP32-WROOM-32S (Espressif Systems, Shanghai, China) | USD 2.37 |
Microphone | MAX4466 (Analog Devices, Norwood, MA, USA) | USD 1.26 |
Temperature sensor | MAX6675 (Analog Devices, Norwood, MA, USA) | USD 1.45 |
Gas sensor | ENS160 (ScioSense, Eindhoven, The Netherlands) | USD 3.61 |
Accelerometer | ADXL345 (Analog Devices, Norwood, MA, USA) | USD 0.94 |
RTC | DS3231 (Maxim Integrated, Norwood, MA, USA) | USD 0.81 |
MicroSD | OEM (generic, Shanghai, China) | USD 2.01 |
BMS | 15W UPS module (generic, Shanghai, China) | USD 3.12 |
OLED | 128 × 64 OEM | USD 2.11 |
Batteries (x2) | 18650 OEM (generic, Shanghai, China) | USD 2.70 |
Joystick | OEM (generic, Shanghai, China) | USD 1.24 |
Switch | OEM (generic, Shanghai, China) | USD 0.25 |
Case | Aluminum (generic, Shanghai, China) | USD 1.80 |
Wires | OEM (generic, Shanghai, China) | USD 0.50 |
Filament | PLA (Polylactic Acid) (generic, Shanghai, China) | USD 1.00 |
Screws | M3 (generic, Shanghai, China) | USD 0.60 |
Total | USD 28.47 |
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Amorim, M.L.M.; Lima, J.G.; Torres, N.N.S.; Afonso, J.A.; Lopes, S.F.; Carmo, J.P.P.d.; Hartmann, L.V.; Souto, C.R.; Salvadori, F.; Ando Junior, O.H. Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing. Inventions 2024, 9, 120. https://doi.org/10.3390/inventions9060120
Amorim MLM, Lima JG, Torres NNS, Afonso JA, Lopes SF, Carmo JPPd, Hartmann LV, Souto CR, Salvadori F, Ando Junior OH. Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing. Inventions. 2024; 9(6):120. https://doi.org/10.3390/inventions9060120
Chicago/Turabian StyleAmorim, Marcio Luís Munhoz, Jorge Gomes Lima, Norah Nadia Sánchez Torres, Jose A. Afonso, Sérgio F. Lopes, João P. P. do Carmo, Lucas Vinicius Hartmann, Cicero Rocha Souto, Fabiano Salvadori, and Oswaldo Hideo Ando Junior. 2024. "Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing" Inventions 9, no. 6: 120. https://doi.org/10.3390/inventions9060120
APA StyleAmorim, M. L. M., Lima, J. G., Torres, N. N. S., Afonso, J. A., Lopes, S. F., Carmo, J. P. P. d., Hartmann, L. V., Souto, C. R., Salvadori, F., & Ando Junior, O. H. (2024). Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing. Inventions, 9(6), 120. https://doi.org/10.3390/inventions9060120