A self-configurable fault detection system for Industrial Ethernet networks

2017 ◽  
Vol 65 (6) ◽  
Author(s):  
Stefan Windmann ◽  
Oliver Niggemann
Keyword(s):  
Fault Detection ◽  
Knowledge Base ◽  
Production Systems ◽  
Detection System ◽  
Industrial Ethernet ◽  
Process Variables ◽  
Automated Production ◽  
Knowledge Based ◽  
Sensor Signals ◽  
Ethernet Network

AbstractIn this paper, a self-configurable fault detection system for automated production systems with Industrial Ethernet is proposed. The scope of the proposed fault detection system are process variables, i.e., the observed actuator and sensor signals. Self-configuration of the fault detection system is enabled by recording and analyzing the link connection of the Ethernet network during system start. In a subsequent training phase, a knowledge base is automatically built from the observed process variables. Knowledge-based fault detection is accomplished once the knowledge base is established. Fault detection has been evaluated for a glue production process. In this application case, the knowledge-based fault detection method yielded a balanced accuracy of 99.81%, while a model-based method, which has been used as reference, produced a balanced accuracy of 93.11%.

scholarly journals Wireless Fault Detection System for an Industrial Robot Based on Statistical Control Chart

2017 ◽  
Vol 7 (6) ◽  
pp. 3421
Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker
Keyword(s):  
Fault Detection ◽  
Control Chart ◽  
Production Systems ◽  
Detection System ◽  
Industrial Robot ◽  
Vibration Signal ◽  
Cost Effective ◽  
Base Station ◽  
Industrial Robots ◽  
Statistical Control

Industrial robots are now commonly used in production systems to improve productivity, quality and safety in manufacturing processes. Recent developments involve using robots cooperatively with production line operatives. Regardless of application, there are significant implications for operator safety in the event of a robot malfunction or failure, and the consequent downtime has a significant impact on productivity in manufacturing. Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation and thus reducing the maintenance costs. Developments in electronics and computing have opened new horizons in the area of condition monitoring. The aim of using wireless electronic systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to develop an online and wireless fault detection system for an industrial robot based on statistical control chart approach. An experimental investigation was accomplished using the PUMA 560 robot and vibration signal capturing was adopted, as it responds immediately to manifest itself if any change is appeared in the monitored machine, to extract features related to the robot health conditions. The results indicate the successful detection of faults at the early stages using the key extracted parameters.

New fault detection and fault-tolerant scheme for Doppler velocity logger outage in ocean navigation systems

2021 ◽  
pp. 1-16
Author(s):  
Mojtaba Hashemi ◽  
Ehsan Shami
Keyword(s):  
Fault Detection ◽  
Fault Tolerant ◽  
Detection System ◽  
Signal Reconstruction ◽  
Gaussian Process Regression ◽  
Doppler Velocity ◽  
Navigation Systems ◽  
Navigation Data ◽  
Knowledge Based ◽  
Abrupt Faults

Abstract The inertial navigation system/Doppler velocity logger (INS/DVL) plays an important role in ocean navigation. Any DVL malfunction poses serious risks to navigation. A precise detection system is required to detect the initial moments of DVL signal malfunctions; moreover, with loss of DVL, a fault-tolerant scheme (FTS) is necessary for DVL signal reconstruction. In this paper, an evolutionary knowledge-based method, namely improved evolutionary TS-fuzzy (I-eTS), is adopted to build an artificial intelligence (AI)-based pseudo DVL to deal with long-term outage of DVL. By employing Gaussian process regression (GPR) models for fault detection, a new FTS is constructed. To verify the effectiveness of the new fault detection and fault tolerance system, navigation data is gathered by a test setup and algorithms are performed in the laboratory. In the tests, it is demonstrated that the proposed FTS leads to rapid detection of both gradual and abrupt faults, which leads to less interaction between fault detection and FTS.

scholarly journals Projections of Local Knowledge-Based Adaptation Strategies of Mexican Coffee Farmers

Climate ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 60
Author(s):  
Patricia Ruiz-García ◽  
Cecilia Conde-Álvarez ◽  
Jesús David Gómez-Díaz ◽  
Alejandro Ismael Monterroso-Rivas
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Local Knowledge ◽  
Production Systems ◽  
Agroforestry Systems ◽  
Southern Mexico ◽  
Knowledge Based ◽  
Coffee Farmers ◽  
Extreme Hydrological Events ◽  
Adaptation Policies

Local knowledge can be a strategy for coping with extreme events and adapting to climate change. In Mexico, extreme events and climate change projections suggest the urgency of promoting local adaptation policies and strategies. This paper provides an assessment of adaptation actions based on the local knowledge of coffee farmers in southern Mexico. The strategies include collective and individual adaptation actions that farmers have established. To determine their viability and impacts, carbon stocks and fluxes in the system’s aboveground biomass were projected, along with water balance variables. Stored carbon contents are projected to increase by more than 90%, while maintaining agroforestry systems will also help serve to protect against extreme hydrological events. Finally, the integration of local knowledge into national climate change adaptation plans is discussed and suggested with a local focus. We conclude that local knowledge can be successful in conserving agroecological coffee production systems.

scholarly journals A Novel Mutual Information and Partial Least Squares Approach for Quality-Related and Quality-Unrelated Fault Detection

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 166
Author(s):  
Majed Aljunaid ◽  
Yang Tao ◽  
Hongbo Shi
Keyword(s):  
Fault Detection ◽  
Mutual Information ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
False Alarms ◽  
Total Quality ◽  
Process Variables ◽  
Tennessee Eastman Process ◽  
Value Decomposition

Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal and residual parts. However, as the principal part still contains many components unrelated to quality, if these components were not removed it could cause many false alarms. Besides, although these components do not affect product quality, they have a great impact on process safety and information about other faults. Removing and discarding these components will lead to a reduction in the detection rate of faults, unrelated to quality. To overcome the drawbacks of Standard PLS, a novel method, MI-PLS (mutual information PLS), is proposed in this paper. The proposed MI-PLS algorithm utilizes mutual information to divide the process variables into selected and residual components, and then uses singular value decomposition (SVD) to further decompose the selected part into quality-related and quality-unrelated components, subsequently constructing quality-related monitoring statistics. To ensure that there is no information loss and that the proposed MI-PLS can be used in quality-related and quality-unrelated fault detection, a principal component analysis (PCA) model is performed on the residual component to obtain its score matrix, which is combined with the quality-unrelated part to obtain the total quality-unrelated monitoring statistics. Finally, the proposed method is applied on a numerical example and Tennessee Eastman process. The proposed MI-PLS has a lower computational load and more robust performance compared with T-PLS and PCR.

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