Prediction of Machine Deterioration Using Vibration Based Fault Trends and Recurrent Neural Networks

1999 ◽  
Vol 121 (3) ◽  
pp. 355-362 ◽  
Author(s):  
P. W. Tse ◽  
D. P. Atherton
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Production Cost ◽  
Market Competition ◽  
Spare Parts ◽  
Remaining Life ◽  
Substantial Portion ◽  
Market Shares ◽  
The Cost ◽  
Machine Deterioration

High market competition for sales requires companies to reduce the cost of production if they are to maintain their market shares. Since the cost of maintenance contributes a substantial portion of the production cost, companies must budget maintenance effectively. Machine deterioration prognosis can decrease the cost of maintenance by minimizing the loss of production due to machine breakdown and avoiding the overstocking of spare parts. A new prognostic method is described in this paper which has been developed to forecast the rate of machine deterioration using recurrent neural networks. From tests applying the method to the prediction of nonlinear sunspot activities and vibration based fault trends of several industrial machines, the results have shown that the method is promising. It not only evaluates the seriousness of damage caused by faults, but also forecasts the remaining life span of defective components.

scholarly journals Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Pornthep Preechayasomboon ◽  
Eric Rombokas
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Models ◽  
Open Loop ◽  
Proof Of Concept ◽  
State Estimator ◽  
Loop Control ◽  
Practical Applications ◽  
Soft Actuator ◽  
The Cost

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

scholarly journals Modelling of the Impact of National Standardization on Duopolistic Competition in the Civil Aircraft Market

2020 ◽  
Vol 17 (4) ◽  
pp. 859-873
Author(s):  
E. V. Varyukhina ◽  
◽  
V. V. Klochkov ◽  
Keyword(s):  
Market Competition ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Equilibrium Degree ◽  
Aviation Noise ◽  
Market Shares ◽  
Civil Aircraft ◽  
Zero Sum ◽  
The Cost ◽  
The Impact

The purpose of this study is to analyze standards as one of the main tools of protectionism in global markets of industrial goods. We use standards for modeling of market competition and adapt this approach for civil aviation markets. The role of local noise standards in civil aircraft markets is discussed. Imposition of more stringent aviation noise standards is modelled in the form of a two-person non-zero-sum game. Players are aircraft corporations that conduct research and development to reduce noise and lobby for stricter regulations in their controlled markets. The model can be used to predict that tighter aviation noise standards will be imposed and to justify the strategy of Russian aviation industry and science. The proposed approach can be adapted for other industries with strict regulations (in terms of safety, ecology). Such estimation allows us to assess whether it is in the country’s interests to participate in the standards race or not. It is shown that the equilibrium degree of standards tightening is higher if the players’ market shares are close to equal or individual players have advantages in the cost of production and/or product improvement is highly likely due to the company’s R&D progress.

scholarly journals Study of the impact of the COVID-19 pandemic on international air transportation

2021 ◽  
Vol 29 (1) ◽  
pp. 22-35
Author(s):  
Eugeny Yu. Shchetinin
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Stock Prices ◽  
Short Term Memory ◽  
Air Transportation ◽  
Time Series Forecasting ◽  
The Cost ◽  
The Impact

Time Series Forecasting has always been a very important area of research in many domains because many different types of data are stored as time series. Given the growing availability of data and computing power in the recent years, Deep Learning has become a fundamental part of the new generation of Time Series Forecasting models, obtaining excellent results.As different time series problems are studied in many different fields, a large number of new architectures have been developed in recent years. This has also been simplified by the growing availability of open source frameworks, which make the development of new custom network components easier and faster.In this paper three different Deep Learning Architecture for Time Series Forecasting are presented: Recurrent Neural Networks (RNNs), that are the most classical and used architecture for Time Series Forecasting problems; Long Short-Term Memory (LSTM), that are an evolution of RNNs developed in order to overcome the vanishing gradient problem; Gated Recurrent Unit (GRU), that are another evolution of RNNs, similar to LSTM.The article is devoted to modeling and forecasting the cost of international air transportation in a pandemic using deep learning methods. The author builds time series models of the American Airlines (AAL) stock prices for a selected period using LSTM, GRU, RNN recurrent neural networks models and compare the accuracy forecast results.

Recurrent Neural Network for Computing Outer Inverse

2016 ◽  
Vol 28 (5) ◽  
pp. 970-998 ◽  
Author(s):  
Ivan S. Živković ◽  
Predrag S. Stanimirović ◽  
Yimin Wei
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Null Space ◽  
Matrix Inversion ◽  
Nonsingular Matrix ◽  
Initial State ◽  
Matrix Operations ◽  
The Matrix ◽  
The Cost

Two linear recurrent neural networks for generating outer inverses with prescribed range and null space are defined. Each of the proposed recurrent neural networks is based on the matrix-valued differential equation, a generalization of dynamic equations proposed earlier for the nonsingular matrix inversion, the Moore-Penrose inversion, as well as the Drazin inversion, under the condition of zero initial state. The application of the first approach is conditioned by the properties of the spectrum of a certain matrix; the second approach eliminates this drawback, though at the cost of increasing the number of matrix operations. The cases corresponding to the most common generalized inverses are defined. The conditions that ensure stability of the proposed neural network are presented. Illustrative examples present the results of numerical simulations.

scholarly journals 3Cnet: Pathogenicity prediction of human variants using knowledge transfer with deep recurrent neural networks

2020 ◽  
Author(s):  
Dhong-gun Won ◽  
Kyoungyeul Lee
Keyword(s):  
Neural Networks ◽  
Knowledge Transfer ◽  
Recurrent Neural Networks ◽  
Performance Comparison ◽  
Training Data ◽  
Pathogenic Variants ◽  
Pathogenicity Prediction ◽  
Amount Of Knowledge ◽  
The Cost ◽  
Next Generation Sequencing Ngs

AbstractThanks to the improvement of Next Generation Sequencing (NGS), genome-based diagnosis for rare disease patients become possible. However, accurate interpretation of human variants requires massive amount of knowledge gathered from previous researches and clinical cases. Also, manual analysis for each variant in the genome of patients takes enormous time and effort of clinical experts and medical doctors. Therefore, to reduce the cost of diagnosis, various computational tools have been developed for the pathogenicity prediction of human variants. Nevertheless, there has been the circularity problem of conventional tools, which leads to the overlap of training data and eventually causes overfitting of algorithms. In this research, we developed a pathogenicity predictor, named as 3Cnet, using deep recurrent neural networks which analyzes the amino-acid context of a missense mutation. 3Cnet utilizes knowledge transfer of evolutionary conservation to train insufficient clinical data without overfitting. The performance comparison clearly shows that 3Cnet can find the true disease-causing variant from a large number of missense variants in the genome of a patient with higher sensitivity (recall = 13.9 %) compared to other prediction tools such as REVEL (recall = 7.5 %) or PrimateAI (recall = 6.4 %). Consequently, 3Cnet can improve the diagnostic rate for patients and discover novel pathogenic variants with high probability.

scholarly journals Can Recurrent Neural Networks Learn Nested Recursion?

2018 ◽  
Vol 16 ◽  
Author(s):  
Jean-Philippe Bernardy
Keyword(s):  
Neural Networks ◽  
Upper Bound ◽  
Recurrent Neural Networks ◽  
Natural Languages ◽  
Relevant Today ◽  
Nested Recursion ◽  
The Cost ◽  
Context Free ◽  
Do So ◽  
Context Free Grammars

Context-free grammars (CFG) were one of the first formal tools used to model natural languages, and they remain relevant today as the basis of several frameworks. A key ingredient of CFG is the presence of nested recursion. In this paper, we investigate experimentally the capability of several recurrent neural networks (RNNs) to learn nested recursion. More precisely, we measure an upper bound of their capability to do so, by simplifying the task to learning a generalized Dyck language, namely one composed of matching parentheses of various kinds. To do so, we present the RNNs with a set of random strings having a given maximum nesting depth and test its ability to predict the kind of closing parenthesis when facing deeper nested strings. We report mixed results: when generalizing to deeper nesting levels, the accuracy of standard RNNs is significantly higher than random, but still far from perfect. Additionally, we propose some non-standard stack-based models which can approach perfect accuracy, at the cost of robustness.

IMPLEMENTASI JOB ORDER COSTING METHOD DALAM PERHITUNGAN HARGA POKOK PRODUKSI MAP

2018 ◽  
Vol 4 (2) ◽  
pp. 43-55
Author(s):  
Ika Yulianti ◽  
Endah Masrunik ◽  
Anam Miftakhul Huda ◽  
Diana Elvianita
Keyword(s):  
Production Process ◽  
Quantitative Data ◽  
Production Cost ◽  
Qualitative Data ◽  
Cost Data ◽  
Method Calculation ◽  
Cost Of Production ◽  
The Right ◽  
The Cost

This study aims to find a comparison of the calculation of the cost of goods manufactured in the CV. Mitra Setia Blitar uses the company's method and uses the Job Order Costing (JOC) method. The method used in this study is quantitative. The types of data used are quantitative and qualitative. Quantitative data is in the form of map production cost data while qualitative data is in the form of information about map production process. The result of calculating the cost of production of the map between the two methods results in a difference of Rp. 306. Calculation using the company method is more expensive than using the Job Order Costing method. Calculation of cost of goods manufactured using the company method is Rp. 2,205,000, - or Rp. 2,205, - each unit. While using the Job Order Costing (JOC) method is Rp. 1,899,000, - or Rp 1,899, - each unit. So that the right method used in calculating the cost of production is the Job Order Costing (JOC) method

Sign in / Sign up

Export Citation Format

Share Document