Development of a Flowmeter Using Vibration Interaction between Gauge Plate and External Flow Analyzed by LSTM

Industry 5.0, also known as the “smart factory,” is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system’s operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

show abstract

“…The equation for the LSTM model can be defined using the mathematical expression below:

where

describes the input of the LSTM architecture cell,

and

represent the hidden states and cell states of the architecture which are documented in several related theories [ 54 , 55 , 56 , 57 ].…”

Section: Theoretical Backgroundsmentioning

confidence: 99%

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Seok

Kareem

Hur

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…i, f , O represents the input, forget, and output gates, x t describes the current input to the LSTM architectural structure, C t , c t−1 , h t , h t−1 represents the cell state, previous cell state, the hidden cell state, and the previous hidden cell state, respectively, and σ, W, b represents the sigmoid function, weight and bias of each gate [49][50][51][52].…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Extruder Machine Gear Fault Detection Using Autoencoder LSTM via Sensor Fusion Approach

Lee,

Okwuosa,

Hur

2023

Inventions

View full text Add to dashboard Cite

In industrial settings, gears play a crucial role by assisting various machinery functions such as speed control, torque manipulation, and altering motion direction. The malfunction or failure of these gear components can have serious repercussions, resulting in production halts and financial losses. To address this need, research efforts have focused on early defect detection in gears in order to reduce the impact of possible failures. This study focused on analyzing vibration and thermal datasets from two extruder machine gearboxes using an autoencoder Long Short-Term Memory (AE-LSTM) model, to ensure that all important characteristics of the system are utilized. Fast independent component analysis (FastICA) is employed to fuse the data signals from both sensors while retaining their characteristics. The major goal is to implement an outlier detection approach to detect and classify defects. The results of this study highlighted the extraordinary performance of the AE-LSTM model, which achieved an impressive accuracy rate of 94.42% in recognizing malfunctioning gearboxes within the extruder machine system. The study used robust global metric evaluation techniques, such as accuracy, F1-score, and confusion metrics, to thoroughly evaluate the model’s dependability and efficiency. LSTM was additionally employed for anomaly detection to further emphasize the adaptability and interoperability of the methodology. This modification yielded a remarkable accuracy of 89.67%, offering additional validation of the model’s reliability and competence.

show abstract

“…It consists of multiple cells, and each LSTM unit cell has an input gate ( i t ), an output gate ( o t ), a forget gate ( f t ), and a memory cell state ( C t ). The cells remember the values over an arbitrary time interval, while the three gates control the flow of information into and out of the cells [ 8 ]. When the input vector constructed with the uroflow sound loudness and roughness [ x t = ( N t , R t )] and the previous output values ( h t −1 ) are given, the first step in the forget gate layer of the LSTM cell is to determine which information is to be thrown away, which is carried out as follows:

…”

Section: Lstm Network For the Luts Health Monitoringmentioning

confidence: 99%

“…Diagnosis of the bladder emptying pattern needs to have information about the flowrate and total flow volume. For outside flows, it is not straightforward to install instruments [ 8 ]. The sounds and vibrations due to urine flows include important information about the health status.…”

Section: Introductionmentioning

confidence: 99%

Classification of Bladder Emptying Patterns by LSTM Neural Network Trained Using Acoustic Signatures

Jin

Chung

Kim

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

(1) Background: Non-invasive uroflowmetry is used in clinical practice for diagnosing lower urinary tract symptoms (LUTS) and the health status of a patient. To establish a smart system for measuring the flowrate during urination without any temporospatial constraints for patients with a urinary disorder, the acoustic signatures from the uroflow of patients being treated for LUTS at a tertiary hospital were utilized. (2) Methods: Uroflowmetry data were collected for construction and verification of a long short-term memory (LSTM) deep-learning algorithm. The initial sample size comprised 34 patients; 27 patients were included in the final analysis. Uroflow sounds generated from flow impacts on a structure were analyzed by loudness and roughness parameters. (3) Results: A similar signal pattern to the clinical urological measurements was observed and applied for health diagnosis. (4) Conclusions: Consistent flowrate values were obtained by applying the uroflow sound samples from the randomly selected patients to the constructed model for validation. The flowrate predicted using the acoustic signature accurately demonstrated actual physical characteristics. This could be used for developing a new smart flowmetry device applicable in everyday life with minimal constraints from settings and enable remote diagnosis of urinary system diseases by objective continuous measurements of bladder emptying function.

show abstract

Development of a Flowmeter Using Vibration Interaction between Gauge Plate and External Flow Analyzed by LSTM

Cited by 10 publications

References 22 publications

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Extruder Machine Gear Fault Detection Using Autoencoder LSTM via Sensor Fusion Approach

Classification of Bladder Emptying Patterns by LSTM Neural Network Trained Using Acoustic Signatures

Contact Info

Product

Resources

About