scholarly journals Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis

2021 ◽  
Vol 11 (2) ◽  
pp. 1697
Author(s):  
Abdallah Zahidi ◽  
Said Amrane ◽  
Nawfel Azami ◽  
Naoual Nasser
Keyword(s):  
Neural Networks ◽  
Optical Fiber ◽  
Coefficient Of Friction ◽  
Failure Modes ◽  
Step Response ◽  
Neural Net ◽  
Hydraulic Systems ◽  
Functional Diagnosis ◽  
Simulink Model ◽  
Future Work

As crucial parts of various engineering systems, solenoid valves (SVs) operated by electromagnetic solenoid (EMS) are of great importance and their failure may lead to cause unexpected casualties. This failure, characterized by a degradation of the performances of the SVs, could be due to a fluctuations in the EMS parameters. These fluctuations are essentially attributed to the changes in the spring constant, coefficient of friction, inductance, and the resistance of the coil. Preventive maintenance by controlling and monitoring these parameters is necessary to avoid eventual failure of these actuators. The authors propose a new methodology for the functional diagnosis of electromagnetic solenoids (EMS) used in hydraulic systems. The proposed method monitors online the electrical and mechanical parameters varying over time by using artificial neural networks algorithm coupled with an optical fiber polarization squeezer based on EMS for polarization scrambling. First, the MATLAB/Simulink model is proposed to analyze the effect of the parameters on the dynamic EMS model. The result of this simulation is used for training the neural network, then a simulation is proposed using the neural net fitting toolbox to determine the solenoid parameters (Resistance of the coil R, stiffness K and coefficient of friction B of the spring) from the coefficients of the transfer function, established from the model step response. Future work will include not only diagnosing failure modes, but also predicting the remaining life based on the results of monitoring.

scholarly journals A Matrix FMEA Analysis of Variable Delivery Vane Pumps

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1741
Author(s):  
Joanna Fabis-Domagala ◽  
Mariusz Domagala ◽  
Hassan Momeni
Keyword(s):  
Risk Analysis ◽  
Failure Modes ◽  
High Reliability ◽  
Matrix Analysis ◽  
Hydraulic Systems ◽  
Vane Pump ◽  
End Effects ◽  
Root Cause ◽  
Preventative Measures ◽  
Fmea Analysis

Hydraulic systems are widely used in the aeronautic, machinery, and energy industries. The functions that these systems perform require high reliability, which can be achieved by examining the causes of possible defects and failures and by taking appropriate preventative measures. One of the most popular methods used to achieve this goal is FMEA (Failure Modes and Effects Analysis), the foundations of which were developed and implemented in the early 1950s. It was systematized in the following years and practically implemented. It has also been standardized and implemented as one of the methods of the International Organization for Standardization (ISO) 9000 series standards on quality assurance and management. Apart from wide application, FMEA has a number of weaknesses, which undoubtedly include risk analysis based on the RPN (Risk Priority Number), which is evaluated as a product of severity, occurrence, and detection. In recent years, the risk analysis has been very often replaced by fuzzy logic. This study proposes the use of matrix analysis and statistical methods for performing simplified RCA (Root Cause Analysis) and for classification potential failures for a variable delivery vane pump. The presented methodology is an extension of matrix FMEA and allows for prioritizing potential failures and their causes in relation to functions performed by pump components, the end effects, and the defined symptoms of failure of the vane pump.

The synthesis method of regulators for multichannel systems using neural networks

2020 ◽  
pp. 111-118
Author(s):  
Александр Александрович Воевода ◽  
Дмитрий Олегович Романников
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Automatic Control ◽  
Control Systems ◽  
Synthesis Method ◽  
Neural Net ◽  
Initial State ◽  
Set Point ◽  
Automatic Control Systems ◽  
Policy Gradient

Синтез регуляторов для многоканальных систем - актуальная и сложная задача. Одним из возможных способов синтеза является применение нейронных сетей. Нейронный регулятор либо обучают на предварительно рассчитанных данных, либо используют для настройки параметров ПИД-регулятора из начального устойчивого положения замкнутой системы. Предложено использовать нейронные сети для регулирования двухканального объекта, при этом обучение будет выполняться из неустойчивого (произвольного) начального положения с применением методов обучения нейронных сетей с подкреплением. Предложена структура нейронной сети и замкнутой системы, в которой уставка задается при помощи входного параметра нейронной сети регулятора The problem for synthesis of automatic control systems is hard, especially for multichannel objects. One of the approaches is the use of neural networks. For the approaches that are based on the use of reinforcement learning, there is an additional issue - supporting of range of values for the set points. The method of synthesis of automatic control systems using neural networks and the process of its learning with reinforcement learning that allows neural networks learning for supporting regulation is proposed in the predefined range of set points. The main steps of the method are 1) to form a neural net input as a state of the object and system set point; 2) to perform modelling of the system with a set of randomly generated set points from the desired range; 3) to perform a one-step of the learning using the Deterministic Policy Gradient method. The originality of the proposed method is that, in contrast to existing methods of using a neural network to synthesize a controller, the proposed method allows training a controller from an unstable initial state in a closed system and set of a range of set points. The method was applied to the problem of stabilizing the outputs of a two-channel object, for which stabilization both outputs and the first near the input set point is required

Failure Modes Model of MEMS Accelerometers Based on Neural Networks

2008 ◽  
Author(s):  
Yanping Bai ◽  
Ping An ◽  
Yilong Hao
Keyword(s):  
Neural Networks ◽  
Failure Modes ◽  
Late Phase ◽  
Primary Sources ◽  
Bp Network ◽  
Bp Networks ◽  
Mems Accelerometers ◽  
Perception Model ◽  
Hidden Layer ◽  
Training Success

Fabrication of a MEMS system involves design, testing, packaging and reliability related issues. However, reliability issues that are discovered at a late phase may cause major delays in the product development going together with high costs. In this paper we study the failure modes and Mechanisms of MEMS accelerometers products and present the classification modeling of failure modes based on neural networks. In ours MEMS accelerometers, there are six failure mechanisms that have been found to be the primary sources of failure nodes. We introduce nonlinear BP network with a hidden layer and linear perception to classify for MEMS accelerometers products. Classification results show that nonlinear BP network seem to be most appropriate to approach the problem of failure modes classification than linear perception. BP neural network is capable of learning the intrinsic relations of the patterns with which they were trained. For all experiments results, the training success of rate is 100% for both methods. BP networks obtained a high forecast success of rate of over 99.5%. The linear perception model obtained a success of rate of over 95.5%. We also analyze the technology stability of MEMS products.

scholarly journals Cluster analysis of knowledge sources in standardized electrical engineering subfields

2016 ◽  
Vol 13 (3) ◽  
pp. 405-422
Author(s):  
Marija Blagojevic ◽  
Zivadin Micic ◽  
Momcilo Vujicic
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Electrical Engineering ◽  
Financial Resources ◽  
Knowledge Sources ◽  
Knowledge Source ◽  
Innovation Intensity ◽  
Future Work ◽  
Analysis Of Knowledge ◽  
Mathematical Relations

The paper presents a cluster analysis of innovation of knowledge sources based on the standards in the field of Electrical Engineering. Both local (SRPS) and global (ISO) knowledge sources have been analysed with the aim of innovating a Knowledge Base (KB). The results presented indicate a means/possibility of grouping the subfields within a cluster. They also point to a trend or intensity of knowledge source innovation for the purpose of innovating the KB that accompanies innovations. The study provides the possibility of predicting necessary financial resources in the forthcoming period by means of original mathematical relations. Furthermore, the cluster analysis facilitates the comparison of the innovation intensity in this and other (sub)fields. Future work relates to the monitoring of the knowledge source innovation by means of KB engineering and improvement of the methodology of prediction using neural networks.

scholarly journals An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

2019 ◽  
Author(s):  
René Janßen ◽  
Jakob Zabel ◽  
Uwe von Lukas ◽  
Matthias Labrenz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Environmental Monitoring ◽  
Microbial Communities ◽  
List Type ◽  
Neural Net ◽  
Next Generation ◽  
Artificial Neural

AbstractArtificial neural networks can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network to support environmental monitoring efforts in case of a contamination event by detecting induced changes towards the microbial communities. The neural net was trained on taxonomic cluster count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of the herbicide glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the clusters primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species in cases of glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.Highlight bullet pointsAn artificial neural net was able to identify glyphosate-affected microbial community assemblages based on next generation sequencing dataDecision-relevant taxonomic clusters can be identified by a stochastically subsetting approachJust a fraction of present clusters is needed for classificationFiltering of input data improves classification

scholarly journals Brake systems: a mind of their own

2021 ◽  
pp. 27-34
Author(s):  
Michael Ellims
Keyword(s):  
Failure Modes ◽  
Electronic Control ◽  
Hydraulic Systems ◽  
Control Software ◽  
The Road ◽  
Current Production ◽  
On The Road ◽  
High Level ◽  
Electronic Sensors ◽  
Systems Failure

Brake systems fitted to current production vehicles are not the relativity straightforward hydraulic systems that many people expect. Rather they have evolved into complex systems which are on their own deliberately capable of affecting the behaviour of a vehicle. Crucially they depend on computers, software and electronic sensors to allow them to form a model of how the vehicle is expected to behave on the road and how it is actually behaving. Like any artefact they can, and do fail. This paper provides a high-level overview of the braking systems currently in place, how these systems act and present some examples of how they have failed in practice. Index words: vehicles; vehicle electronics; electronic control; software; brake systems; failure modes

Optical neural networks based on optical fiber-communication system

2019 ◽  
Vol 364 ◽  
pp. 239-244 ◽  
Author(s):  
Tian-You Cheng ◽  
Da-Ya Chou ◽  
Ching-Chuan Liu ◽  
Ya-Ju Chang ◽  
Chii-Chang Chen
Keyword(s):  
Neural Networks ◽  
Optical Fiber ◽  
Communication System ◽  
Optical Fiber Communication ◽  
Fiber Communication ◽  
Optical Neural Networks ◽  
Optical Fiber Communication System
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