Implementation of Raspberry Pi for Fault Detection in Optic Fibre Line

2020 ◽  
pp. 30-36
Author(s):  
K. P. Swain ◽  
S. R. Das ◽  
Sangram Kishore Mohanty ◽  
G. Palai
Keyword(s):  
Fault Detection ◽  
Raspberry Pi ◽  
Optic Fibre

scholarly journals Design and Development of Techniques for Equipment Health Monitoring System

2019 ◽  
Vol 9 (2S) ◽  
pp. 277-282
Keyword(s):  
Fault Detection ◽  
Embedded System ◽  
Health Monitoring ◽  
Conveyor Belt ◽  
Unmanned Vehicles ◽  
Sensor Data ◽  
Maintenance Cost ◽  
Raspberry Pi ◽  
Markovian Model ◽  
Time Data

Machines in Industries are often subjected to enormous wear and tear, which if unnoticed, may lead to production delays and increased maintenance cost. Machines must be able to analyse and provide statistics about its health, so that preventive measures can be taken to avoid catastrophes in the industries. Thus, there is a need of automated fault detection and prediction of system’s condition. The concept of equipment health monitoring is a crucial step in the field of research and development in the manufacturing industries. This equipment makes it handy in situations where machines require continuous monitoring and is difficult for humans to provide such attention , especially in the case of unmanned vehicles. Prediction of the status of equipment by acquisition of data from industrial machinery is the critical step in building such a system. Health of machines can be estimated by the data collected by the sensors-temperature, accelerometer, etc integrated with an embedded computing system, like a Raspberry Pi. This IoT model consisting of embedded system with wireless connectivity collects real time data from the equipment/machinery used in industries. This data is used to analyse and predict the health of the equipment, examine the steady-state characteristics using Machine Learning technique, Hidden Markovian Model. The concept of the proposed IoT model is evaluated over a conveyor belt test rig under various conditions, like different loads placed on various locations of conveyor belt and the belt is made to run at different speeds and data is collected over all these conditions. Then, a data model is created using Hidden Markovian Model which is further used in predicting the state of the belt based on the sequential data, here it is the sensor data. Given a state of the belt, this model can predict whether the belt is in proper condition or not, and if human intervention is required. Thus, at any point of time, having this setup on the machinery which needs to be monitored can be used in predicting the faults and notifying the user in case of any faulty behaviour or malfunctioning of machines. This setup can be used for any machines which are subjected to any motion, vibration and thermal changes. This helps in creating a completely automated fault detection systems in the present Industries.

scholarly journals An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software

2020 ◽  
Vol 10 (14) ◽  
pp. 4696 ◽  
Author(s):  
Luis I. Minchala ◽  
Jonnathan Peralta ◽  
Paul Mata-Quevedo ◽  
Jaime Rojas
Keyword(s):  
Fault Detection ◽  
Open Source ◽  
Low Cost ◽  
Fault Detection And Diagnosis ◽  
Raspberry Pi ◽  
Enabling Factors ◽  
Pid Algorithm ◽  
Embedded Platforms ◽  
Detection And Diagnosis ◽  
And Control

This paper presents a performance evaluation of the development of the instrumentation, communications and control systems of a two-tank process by using low-cost hardware and open source software. The hardware used for automating this process consists of embedded platforms (Arduino and Raspberry Pi) integrated into programmable logic controllers (PLCs), which are connected to a supervisory control and data acquisition (SCADA) system implemented with an open source Industrial Internet of Things (IIoT) platform. The main purpose of the proposed approach is to evaluate low-cost automation solutions (hardware and software) within the framework of modern industry requirements in order to determine whether these technologies could be enabling factors of IIoT. The proposed control strategy for regulating tank levels combines the classic PID algorithm and the fuzzy gain scheduling PID (FGS-PID) approach. Fault detection capabilities are also enabled for the system through a fault detection and diagnosis module (FDD) implemented with an extended Kalman filter (EKF). The distributed controller’s (DC) algorithms are embedded into the PLC’s processors in order to demonstrate the flexibility of the proposed system. Additionally, a remote human to machine interface (HMI) is deployed through a web client of the IIoT application. Experimental results show the proper operation of the overall system.

scholarly journals A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3066
Author(s):  
Egidio D’Amato ◽  
Vito Antonio Nardi ◽  
Immacolata Notaro ◽  
Valerio Scordamaglia
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Flight Control ◽  
Particle Filtering ◽  
Low Cost ◽  
Experimental Tests ◽  
Attitude Estimation ◽  
Fault Detection And Isolation ◽  
Raspberry Pi ◽  
Filter Approach

Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

scholarly journals Image-Processing-Based Low-Cost Fault Detection Solution for End-of-Line ECUs in Automotive Manufacturing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3520 ◽  
Author(s):  
Adrian Korodi ◽  
Denis Anitei ◽  
Alexandru Boitor ◽  
Ioan Silea
Keyword(s):  
Image Processing ◽  
Fault Detection ◽  
Experimental Model ◽  
Manufacturing Industry ◽  
Electronic Control Unit ◽  
Low Cost ◽  
Control Unit ◽  
Raspberry Pi ◽  
Automotive Manufacturing ◽  
The Impact

The manufacturing industry is continuously researching and developing strategies and solutions to increase product quality and to decrease production time and costs. The approach is always targeting more automated, traceable, and supervised production, minimizing the impact of the human factor. In the automotive industry, the Electronic Control Unit (ECU) manufacturing ends with complex testing, the End-of-Line (EoL) products being afterwards sent to client companies. This paper proposes an image-processing-based low-cost fault detection (IP-LC-FD) solution for the EoL ECUs, aiming for high-quality and fast detection. The IP-LC-FD solution approaches the problem of determining, on the manufacturing line, the correct mounting of the pins in the locations of each connector of the ECU module, respectively, other defects as missing or extra pins, damaged clips, or surface cracks. The IP-LC-FD system is a hardware–software structure, based on Raspberry Pi microcomputers, Pi cameras, respectively, Python and OpenCV environments. This paper presents the two main stages of the research, the experimental model, and the prototype. The rapid integration into the production line represented an important goal, meaning the accomplishment of the specific hard acceptance requirements regarding both performance and functionality. The solution was implemented and tested as an experimental model and prototype in a real industrial environment, proving excellent results.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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