Fault detection - Diagnosis and predictive maintenance

1986 ◽  
Author(s):  
Andre Rault ◽  
Chrysostome Baskiotis
Keyword(s):  
Fault Detection ◽  
Predictive Maintenance

Decision Versus Identification Issues in Fault Detection/Isolation for Predictive Maintenance

1994 ◽  
Vol 27 (8) ◽  
pp. 1063-1068
Author(s):  
M. Basseville ◽  
A. Benveniste ◽  
Q. Zhang
Keyword(s):  
Fault Detection ◽  
Predictive Maintenance

Possibilities and Limitations of Early Fault Detection in Gas Turbines

2020 ◽  
Author(s):  
Bernhard Persigehl ◽  
Thomas Gellermann ◽  
Stefan Thumm ◽  
Johannes Stoiber
Keyword(s):  
Fault Detection ◽  
Gas Turbines ◽  
Detection System ◽  
Predictive Maintenance ◽  
Damage Scenarios ◽  
Root Cause ◽  
Maximum Loss ◽  
Measuring Devices ◽  
Condition Monitoring Systems ◽  
Early Fault Detection

Abstract Since the 1960’s the maintenance concepts of gas turbines have remarkably changed. Nowadays, condition monitoring systems supervise modern gas turbines. These systems are capable of avoiding failures that are developing slowly. The actual enhancement of early fault detection is predictive maintenance where big data from different sources is used to predict the behavior of gas turbines. In this paper eight different actual gas turbine failures are presented, beginning with the damage event and the root cause and followed by a discussion, whether the failure could have been avoided by predictive maintenance. In some cases, e.g. during commissioning, a successful early fault detection with predictive maintenance cannot be achieved, since the failure often occurs suddenly and is unforeseen. In others cases, especially during operation, the failure could have been prevented by predictive maintenance. However, it has to be stated, in all these cases significantly more measuring devices would have been necessary. The potential exists that more failures can be avoided by predictive maintenance in the future. However, there are also damage scenarios that cannot be prevented by any early fault detection system. Thus, the Possible Maximum Loss, an important figure for technical insurances, remains unchanged independent of the application of predictive maintenance.

Towards Generalization of Intelligent Fault Detection for Roller Element Bearings via Distinct Dataset Transfer Learning

2021 ◽  
Author(s):  
Justin Larocque-Villiers ◽  
Patrick Dumond
Keyword(s):  
Fault Detection ◽  
Open Source ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Operating Conditions ◽  
Predictive Maintenance ◽  
Training Data ◽  
Fault Classification ◽  
Target Domain ◽  
Bearing Faults

Abstract Through the intelligent classification of bearing faults, predictive maintenance provides for the possibility of service schedule, inventory, maintenance, and safety optimization. However, real-world rotating machinery undergo a variety of operating conditions, fault conditions, and noise. Due to these factors, it is often required that a fault detection algorithm perform accurately even on data outside its trained domain. Although open-source datasets offer an incredible opportunity to advance the performance of predictive maintenance technology and methods, more research is required to develop algorithms capable of generalized intelligent fault detection across domains and discrepancies. In this study, current benchmarks on source–target domain discrepancy challenges are reviewed using the Case Western Reserve University (CWRU) and the Paderborn University (PbU) datasets. A convolutional neural network (CNN) architecture and data augmentation technique more suitable for generalization tasks is proposed and tested against existing benchmarks on the Pb U dataset by training on artificial faults and testing on real faults. The proposed method improves fault classification by 13.35%, with less than half the standard deviation of the compared benchmark. Transfer learning is then used to leverage the larger PbU dataset in order to make predictions on the CWRU dataset under a challenging source-target domain discrepancy in which there is minimal training data to adequately represent unseen bearing faults. The transfer learning-based CNN is found to be capable of generalizing across two open-source datasets, resulting in an improvement in accuracy from 53.1% to 68.3%.

scholarly journals Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 586
Author(s):  
Alberto Gascón ◽  
Roberto Casas ◽  
David Buldain ◽  
Álvaro Marco
Keyword(s):  
Fault Detection ◽  
Smart City ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Climate Control ◽  
Sensors And Actuators ◽  
Limited Memory ◽  
Processing Power ◽  
Great Relevance ◽  
Network Good

Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback–Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines.

scholarly journals A Fault Diagnostic Scheme for Predictive Maintenance of AC/DC Converters in MV/LV Substations

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7668
Author(s):  
Giovanni Betta ◽  
Domenico Capriglione ◽  
Luigi Ferrigno ◽  
Marco Laracca ◽  
Gianfranco Miele ◽  
...  
Keyword(s):  
Fault Detection ◽  
Diagnostic Performance ◽  
Cost Effective ◽  
Test Site ◽  
Predictive Maintenance ◽  
Fault Detection And Isolation ◽  
Distribution Grid ◽  
Service Interruption ◽  
Monitoring Process ◽  
Smart Distribution Grid

The reliability of systems and components is a fundamental need for the efficient development of a smart distribution grid. In fact, the presence of a fault in one component of the grid could potentially lead to a service interruption and loss of profit. Since faults cannot be avoided, the introduction of a diagnostic scheme could predict the fault of a component in order to carry out predictive maintenance. In this framework, this paper proposes a novel Fault Detection and Isolation (FDI) scheme for AC/DC converters in MV/LV substations. In order to improve the reliability of the FDI procedure, the system architecture includes also an Instrument Fault Detection and Isolation section for identifying faults that could occur on the instruments and sensors involved in the monitoring process of the AC/DC converter. The proposed architecture is scalable, easily upgradable, and uses cost-effective sensors. Tests, carried out on a real test site, have demonstrated the efficacy of the proposal showing very good IFDI diagnostic performance for the 12 types of faults tested. Furthermore, as the FDI diagnostic performance regards, it shows a detection rate close to 100%.

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