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 Fault Detection and Diagnosis of Air Handling Unit: A Review

2019 ◽  
Vol 255 ◽  
pp. 06001 ◽  
Author(s):  
Cheng Yew Leong
Keyword(s):  
Fault Detection ◽  
Air Conditioning ◽  
Early Stage ◽  
Fault Detection And Diagnosis ◽  
Commercial Building ◽  
Air Handling Unit ◽  
Operations And Maintenance ◽  
Detection And Diagnosis ◽  
Air Conditioning Systems ◽  
And Control

Air-conditioning systems consumed the most energy usage nearly 45% of the total energy used in commercial-building. Where AHU is one of the most extensively operated equipment and this device is typical customize and complex which can results in hardwire failure and controller errors. The efficiency of the system is very much depending on the proper functioning of sensors. Faults arising from the sensors and control systems are a major contribution to the energy wastage. As such faults often go unnoticed for extended periods of time until the deterioration in performance becomes great enough to trigger comfort complaints or total equipment failure. Energy could be reduced if those faults can be detected and identified at early stage. This paper aims to review of various existing automated fault detection and diagnosis (AFDD) methods for an Air Handling Unit. The background of AHU system, general fault detection and diagnosis framework and typical faults in AHU is described. Comparison and evaluation of the various methodologies will be reviewed in this paper. This comparative study also reveals the strengths and weaknesses of the different approaches. The important role of fault diagnosis in the broader context of air- conditioning is also outlined. By identifying and diagnosing faults to be repaired, these techniques can benefits building owners by reducing energy consumption, improving indoor air quality and operations and maintenance.

DISTINGUISHING SENSOR FAULTS FROM SYSTEM FAULTS BY UTILIZING MINIMUM SENSOR REDUNDANCY

2017 ◽  
Vol 41 (3) ◽  
pp. 469-487
Author(s):  
Morteza Taiebat ◽  
Farrokh Sassani
Keyword(s):  
Fault Detection ◽  
Minimum Degree ◽  
A Priori ◽  
Fault Detection And Diagnosis ◽  
A Priori Knowledge ◽  
Sensor Faults ◽  
Control Valves ◽  
Monitoring Process ◽  
Detection And Diagnosis ◽  
And Control

Automated Fault Detection and Diagnosis (FDD) systems depend entirely on the reliability of sensor readings. This paper fills an important gap in the literature by pinpointing the distinction between sensor faults and system faults in the monitoring process. The proposed methodology determines the minimum degree of sensor redundancy necessary to achieve this. A priori knowledge of physical relationships between monitored variables is used to check the credibility of sensor observations. The generalization reveals that for serially connected systems if the number of sensors is greater than 1.5 times of the number of monitored variables, the task of distinguishing between sensor and system faults can be accomplished with certainty, as long as serial causality is valid between the monitored variables. This is verified using a system of interconnected multi reservoirs and control valves.

Industrial fault diagnosis: Pneumatic train door case study

2002 ◽  
Vol 216 (3) ◽  
pp. 175-183 ◽  
Author(s):  
N Lehrasab ◽  
H. P. B. Dassanayake ◽  
C Roberts ◽  
S Fararooy ◽  
C. J. Goodman
Keyword(s):  
Fault Detection ◽  
Low Cost ◽  
Network Models ◽  
Fault Detection And Diagnosis ◽  
Local Data ◽  
Neural Network Models ◽  
Transit Systems ◽  
Detection Scheme ◽  
Detection And Diagnosis

A practical, robust method of fault detection and diagnosis of a class of pneumatic train door commonly found in rapid transit systems is presented. The methodology followed is intended to be applied within a practical system where computation is distributed across a local data network for economic reasons. The health of the system is ascertained by extracting features from the trajectory profiles of the train door. This is incorporated into a low-level fault detection scheme, which relies upon using simple parity equations. Detailed diagnostics are carried out once a fault has been detected; for this purpose neural network models are utilized. This method of detection and diagnosis is implemented in a distributed architecture resulting in a practical, low-cost industrial solution. It is feasible to integrate the results of the diagnosis process directly into an operator's maintenance information system (MIS), thus producing a proactive maintenance regime.

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