Real-Time Wellhead Bending Moment Measurement Using Motion Reference Unit (MRU) Sensors and Machine Learning

2018 ◽  
Author(s):  
Rohit Srikonda ◽  
Rune Haakonsen ◽  
Massimiliano Russo ◽  
Peri Periyasamy
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Real Time ◽  
Mean Squared Error ◽  
Bending Moment ◽  
Sensor Data ◽  
Fe Model ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Tension Time

In order to facilitate real-time monitoring of accumulated wellhead fatigue damage, it is necessary to measure the wellhead bending moment in real-time. This paper presents a novel method to estimate the wellhead bending moment in realtime using acceleration and inclination data from the motion reference unit (MRU) sensors installed on BOP and LRJ, riser tension data and a trained neural network model. The method proposed in this paper is designed with a Recursive Neural Network (RNN) model to be trained to estimate the wellhead bending moment in real-time with high accuracy based on motion MRU sensor data and riser tension time series of a few previous cycles. In addition to the power of modeling complex nonlinearities, RNNs provide the advantage of better capturing the dynamic effects by learning to recognize the patterns in the sensor data and riser tension time series. The RNN model is trained using virtual sensor data and wellhead bending moment from a finite element (FE) model of the drilling riser subjected to irregular wave time domain analyses based on a training matrix with limited number of significant height (Hs) and peak period (Tp) combinations. Once trained, tested and deployed, the RNN model can make real-time estimation of the wellhead bending moment based on MRU sensor data and riser tension time series. The RNN model can be an efficient and accurate alternative to a physical model based on the indirect method for real-time calculation of wellhead bending moment using real-time sensor data. A case study is presented to explain the training procedures for the RNN model. A set of test cases that are not included in the training dataset are used to demonstrate the accuracy of the RNN model using Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE) and coefficient of determination (R2) as a metrics.

scholarly journals Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Neural Network

2018 ◽  
Vol 4 (1) ◽  
pp. 24
Author(s):  
Imam Halimi ◽  
Wahyu Andhyka Kusuma
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Composite Index ◽  
T Test ◽  
Sliding Windows ◽  
Root Mean Squared Error ◽  
Squared Error

Investasi saham merupakan hal yang tidak asing didengar maupun dilakukan. Ada berbagai macam saham di Indonesia, salah satunya adalah Indeks Harga Saham Gabungan (IHSG) atau dalam bahasa inggris disebut Indonesia Composite Index, ICI, atau IDX Composite. IHSG merupakan parameter penting yang dipertimbangkan pada saat akan melakukan investasi mengingat IHSG adalah saham gabungan. Penelitian ini bertujuan memprediksi pergerakan IHSG dengan teknik data mining menggunakan algoritma neural network dan dibandingkan dengan algoritma linear regression, yang dapat dijadikan acuan investor saat akan melakukan investasi. Hasil dari penelitian ini berupa nilai Root Mean Squared Error (RMSE) serta label tambahan angka hasil prediksi yang didapatkan setelah dilakukan validasi menggunakan sliding windows validation dengan hasil paling baik yaitu pada pengujian yang menggunakan algoritma neural network yang menggunakan windowing yaitu sebesar 37,786 dan pada pengujian yang tidak menggunakan windowing sebesar 13,597 dan untuk pengujian algoritma linear regression yang menggunakan windowing yaitu sebesar 35,026 dan pengujian yang tidak menggunakan windowing sebesar 12,657. Setelah dilakukan pengujian T-Test menunjukan bahwa pengujian menggunakan neural network yang dibandingkan dengan linear regression memiliki hasil yang tidak signifikan dengan nilai T-Test untuk pengujian dengan windowing dan tanpa windowing hasilnya sama, yaitu sebesar 1,000.

A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

2020 ◽  
Author(s):  
Meenakshi Narayan ◽  
Ann Majewicz Fey
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Real Time ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Force Sensor ◽  
Sensor Data ◽  
Series Data ◽  
Model Free ◽  
Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

scholarly journals Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series

2009 ◽  
Vol 2009 ◽  
pp. 1-21
Author(s):  
Sanjay L. Badjate ◽  
Sanjay V. Dudul
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Chaotic Time Series ◽  
Nonstationary Time Series ◽  
Medium Term ◽  
Short Term ◽  
Squared Error ◽  
Wide Range

Multistep ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in the recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multistep chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present some disadvantages when long-term prediction is required. In this paper focused time-lagged recurrent neural network (FTLRNN) model with gamma memory is developed for different prediction horizons. It is observed that this predictor performs remarkably well for short-term predictions as well as medium-term predictions. For coupled partial differential equations generated chaotic time series such as Mackey Glass and Duffing, FTLRNN-based predictor performs consistently well for different depths of predictions ranging from short term to long term, with only slight deterioration after k is increased beyond 50. For real-world highly complex and nonstationary time series like Sunspots and Laser, though the proposed predictor does perform reasonably for short term and medium-term predictions, its prediction ability drops for long term ahead prediction. However, still this is the best possible prediction results considering the facts that these are nonstationary time series. As a matter of fact, no other NN configuration can match the performance of FTLRNN model. The authors experimented the performance of this FTLRNN model on predicting the dynamic behavior of typical Chaotic Mackey-Glass time series, Duffing time series, and two real-time chaotic time series such as monthly sunspots and laser. Static multi layer perceptron (MLP) model is also attempted and compared against the proposed model on the performance measures like mean squared error (MSE), Normalized mean squared error (NMSE), and Correlation Coefficient (r). The standard back-propagation algorithm with momentum term has been used for both the models.

THE IMPROVED BPNN-NAR AND BPNN-NARMA MODELS ON MALAYSIAN AGGREGATE COST INDICES WITH OUTLYING DATA

2016 ◽  
Vol 78 (12-3) ◽  
Author(s):  
Saadi Ahmad Kamaruddin ◽  
Nor Azura Md Ghani ◽  
Norazan Mohamed Ramli
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Series Data ◽  
Backpropagation Neural Network ◽  
Data Set ◽  
Squared Error ◽  
The Impact ◽  
Nonlinear Autoregressive

Neurocomputing have been adapted in time series forecasting arena, but the presence of outliers that usually occur in data time series may be harmful to the data network training. This is because the ability to automatically find out any patterns without prior assumptions and loss of generality. In theory, the most common training algorithm for Backpropagation algorithms leans on reducing ordinary least squares estimator (OLS) or more specifically, the mean squared error (MSE). However, this algorithm is not fully robust when outliers exist in training data, and it will lead to false forecast future value. Therefore, in this paper, we present a new algorithm that manipulate algorithms firefly on least median squares estimator (FFA-LMedS) for  Backpropagation neural network nonlinear autoregressive (BPNN-NAR) and Backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) models to reduce the impact of outliers in time series data. The performances of the proposed enhanced models with comparison to the existing enhanced models using M-estimators, Iterative LMedS (ILMedS) and Particle Swarm Optimization on LMedS (PSO-LMedS) are done based on root mean squared error (RMSE) values which is the main highlight of this paper. In the meanwhile, the real-industrial monthly data of Malaysian Aggregate cost indices data set from January 1980 to December 2012 (base year 1980=100) with different degree of outliers problem is adapted in this research. At the end of this paper, it was found that the enhanced BPNN-NARMA models using M-estimators, ILMedS and FFA-LMedS performed very well with RMSE values almost zero errors. It is expected that the findings would assist the respected authorities involve in Malaysian construction projects to overcome cost overruns.

scholarly journals Implementation of a Demand-Side Management Solution for South Korea’s Demand Response Program

2020 ◽  
Vol 10 (5) ◽  
pp. 1751 ◽  
Author(s):  
Wonsuk Ko ◽  
Hamsakutty Vettikalladi ◽  
Seung-Ho Song ◽  
Hyeong-Jin Choi
Keyword(s):  
South Korea ◽  
Real Time ◽  
Demand Response ◽  
Mean Squared Error ◽  
Demand Side Management ◽  
Demand Side ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Demand Response Program ◽  
Relative Root

In this paper, we show the development of a demand-side management solution (DSMS) for demand response (DR) aggregator and actual demand response operation cases in South Korea. To show an experience, Korea’s demand response market outline, functions of DSMS, real contracted capacity, and payment between consumer and load aggregator and DR operation cases are revealed. The DSMS computes the customer baseline load (CBL), relative root mean squared error (RRMSE), and payments of the customers in real time. The case of 10 MW contracted customers shows 108.03% delivery rate and a benefit of 854,900,394 KRW for two years. The results illustrate that an integrated demand-side management solution contributes by participating in a DR market and gives a benefit and satisfaction to the consumer.

Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles

2020 ◽  
pp. 095965182097552
Author(s):  
Wael Farag
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Mean Squared Error ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Root Mean Squared Error ◽  
Time Performance ◽  
Detection And Tracking ◽  
Squared Error

In this article, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. This method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the Unscented Kalman Filter fusion is compared to that of the Extended Kalman Filter fusion showing its superiority. The Unscented Kalman Filter has outperformed the Extended Kalman Filter on all test cases and all the state variable levels (−24% average Root Mean Squared Error). The employed fusion technique shows how outstanding is the improvement in tracking performance compared to the use of a single device (−29% Root Mean Squared Error with lidar and −38% Root Mean Squared Error with radar).

scholarly journals Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR

2021 ◽  
Vol 15 (4) ◽  
pp. 1-28
Author(s):  
Ajitkumar Sureshrao Shitole ◽  
Manoj Himmatrao Devare
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
Local System ◽  
Sensor Data ◽  
Physical Location ◽  
Squared Error ◽  
And Storage ◽  
Auto Regression

This study shows an enhancement of IoT which gets sensor data and performs real-time face recognition to screen physical areas to find strange situations and send an alarm mail to the client to make remedial moves to avoid any potential misfortune in the environment. Sensor data is pushed onto the local system and GoDaddy Cloud, whenever the camera detects a person to optimize the Physical Location Monitoring System by reducing the bandwidth requirement and storage cost onto the Cloud using edge computation. The study reveals that Decision Tree (DT) and Random Forest give reasonably similar macro average f1-score to predict a person using sensor data. Experimental results show that DT is the most reliable predictive model for the Cloud datasets of three different physical locations to predict a person using timestamp with an accuracy of 83.99%, 88.92%, and 80.97%. This study also explains multivariate time series prediction using Vector Auto Regression that gives reasonably good Root Mean Squared Error to predict Temperature, Humidity, Light Dependent Resistor, and Gas time series.

scholarly journals Validasi Data Curah Hujan Satelit TRMM (Tropical Rainfall Measuring Mission) dengan Data Pos Penakar Hujan di DAS Grindulu, Kabupaten Pacitan, Jawa Timur

2021 ◽  
Vol 1 (2) ◽  
pp. 772-785
Author(s):  
Dieta Putri Jarwanti ◽  
◽  
Ery Suhartanto ◽  
Jadfan Sidqi Fidari ◽  
◽  
...  
Keyword(s):  
Real Time ◽  
Mean Squared Error ◽  
Tropical Rainfall Measuring Mission ◽  
Tropical Rainfall ◽  
Root Mean Squared Error ◽  
Squared Error

Pos penakar hujan di Indonesia lokasinya masih kurang tersebar merata, padahal data hujan yang dihasilkan sangat penting. Maka diperlukan analisis validasi dengan data satelit TRMM karena dapat mencakup wilayah luas, tersedia secara near real-time dan aksesnya yang cepat. Penelitian ini bertujuan untuk memvalidasi data satelit dengan data observasi di DAS Grindulu yang datanya dianggap lengkap dan dapat diandalkan. Nantinya digunakan untuk mengantisipasi data curah hujan observasi yang mungkin error atau tidak tersedia. Metode validasi yang digunakan berupa Root Mean Squared Error (RMSE), Uji Kesalahan Relatif (KR), Nash Sutcliffe Efficiency (NSE) serta Koefisien Korelasi (R). Penelitian ini menggunakan dua tahap perhitungan, yaitu analisis validasi data tidak terkoreksi dan data terkoreksi, dimana data terkoreksi dilakukan kalibrasi data terlebih dahulu, hasil dari validasi data TRMM terkoreksi terbaik terdapat pada periode bulanan dengan rentang kalibrasi 9 tahun dan validasi 1 tahun dengan hasil NSE = 0,929; R = 0,969; RMSE = 46,48; KR = 8,9%. Hasil tersebut menunjukkan bahwa data TRMM terkoreksi menghasilkan nilai yang lebih baik dibandingan data TRMM tidak terkoreksi karena memiliki nilai NSE dan R yang mendekati satu dan nilai RMSE dan Kesalahan Relatifnya rendah. Secara kesluruhan, dapat disimpulkan bahwa data TRMM dapat digunakan sebagai data alternatif hidrologi di DAS Grindulu.

scholarly journals Estimation of Worker Fruit-Picking Rates with an Instrumented Picking Bag

2020 ◽  
Vol 63 (6) ◽  
pp. 1913-1924
Author(s):  
Zhenghao Fei ◽  
John Shepard ◽  
Stavros G. Vougioukas
Keyword(s):  
Mean Squared Error ◽  
Inertial Sensor ◽  
Fruit Weight ◽  
Sensor Data ◽  
List Type ◽  
Heavy Load ◽  
Workforce Management ◽  
Root Mean Squared Error ◽  
Light Load ◽  
Squared Error

HighlightsWe designed a low-cost instrumented picking bag that can monitor the worker’s fruit picking process.The bag can be used to estimate the worker’s picking rate for better workforce management.The bag can also be used to estimate the accumulated fruit weight and generate a yield map for orchard management.The best root mean squared error over the entire measurement range was 0.36 kg (1.8% of bag capacity).Abstract. Estimating and recording a worker’s picking rate during tree fruit harvesting can provide useful information for better workforce management, orchard platform crew management, and generation of yield maps (in combination with position). A commercial picking bag was instrumented to estimate harvested fruit weight in real-time. All electronics were placed inside an enclosure that was placed between the bag and its shoulder straps, without hindering picking motions. The electronics included two load cells to measure the forces exerted on the straps by the bag and fruits, an Arduino microcontroller, signal conditioning circuits, data storage, wireless communication components, and inertial sensors. Software was developed for data acquisition, filtering, transmission, and storage. Two calibration models were developed to estimate fruit weight. One model (model 2) used inertial sensor data to compensate for the picking bag’s angle with respect to gravity direction, while the other model (model 1) did not. Dynamic calibration experiments were performed over the entire weight range of the bag (0 to 20 kg) with reference objects of known weight (baseballs and fresh apples). The weight was divided into three ranges: light load (<8 kg), medium load (8 to 13 kg), and heavy load (>13 kg). Results showed that model 1 performed slightly better in the light load range, but model 2 was superior in the medium and heavy load ranges, presumably due to bag angle compensation. The best root mean squared error over the entire range was achieved by model 2 and was 0.36 kg (1.8% of bag capacity). In an application case study, two bags were used by workers harvesting from a platform in a commercial apple orchard. From the data, the pickers’ harvesting speeds were estimated, and the fruit yield distribution was calculated for one side of a tree row. Keywords: Calibration, Electronics, Fruit harvesting, Labor, Yield monitor.

scholarly journals PREDIKSI DATA TRANSAKSI PENJUALAN TIME SERIES MENGGUNAKAN REGRESI LSTM

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Marie Luthfi Ashari ◽  
Mujiono Sadikin
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Long Short Term Memory

Sebagai upaya untuk memenangkan persaingan di pasar, perusahaan farmasi harus menghasilkan produk obat – obatan yang berkualitas. Untuk menghasilkan produk yang berkualitas, diperlukan perencanaan produksi yang baik dan efisien. Salah satu dasar perencanaan produksi adalah prediksi penjualan. PT. Metiska Farma telah menerapkan metode prediksi dalam proses produksi, akan tetapi prediksi yang dihasilkan tidak akurat sehingga menyebabkan tidak optimal dalam memenuhi permintaan pasar. Untuk meminimalisir masalah kurang akuratnya proses prediksi tersebut, dalam penelitian yang disajikan pada makalah ini dilakukan uji coba prediksi menggunakan teknik Machine Learning dengan metode Regresi Long Short Term Memory (LSTM). Teknik yang diusulkan diuji coba menggunakan dataset penjualan produk “X” dari PT. Metiska Farma dengan parameter kinerja Root Mean Squared Error (RMSE) dan MAPE (Mean Absolute Percentage Error). Hasil penelitian ini berupa nilai rata – rata evaluasi error dari pemodelan data training dan data testing. Di mana hasil menunjukan bahwa Regresi LSTM memiliki nilai prediksi penjualan dengan evaluasi model melalui RMSE sebesar 286.465.424 untuk data training dan 187.013.430 untuk data testing. Untuk nilai MAPE sebesar 787% dan 309% untuk data training dan data testing secara berurut.

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