Door Systems Research Articles

The Role of Emergency Generator during Black-Out on the MV. Kelud

Emergency generator is an auxiliary machine that functions to generate electrical power to meet the electrical needs of the ship during an emergency (black-out). This research was conducted to find out what caused the black-out onboard KM. Kelud, how the role of the Emergency generator during a black-out, and how to maintain the emergency generator so that it is always ready when used. The research method that the author uses is a qualitative research method. Data collection is done through interviews and observations. The results obtained from this study indicate that the cause of the black-out on KM. Kelud is the entry of mud into the hull, causing the cooling system in the main engine to be disrupted and eventually black-out. The role of the emergency generator during a black-out is as a temporary source of electricity to run several devices, such as navigation equipment, ship lighting, fire alarm systems, sprinkle pumps, and watertight door systems. In order for the emergency generator to run according to its function, it must be carried out routine maintenance such as checking batteries, oil and lubricants, cooling systems, and periodic testing.

Voice Recognition to Classify “Buka” and “Tutup” Sound to Open and Closes Door Using Mel Frequency Cepstral Coefficients (MFCC) and Convolutional Neural Network (CNN)

The consequences of the coronavirus called COVID-19 have been really impactful on society. Many things need to be changed in order to survive this pandemic. People have to avoid physical contact to minimize the probability of getting caught by other people who have been infected. A doorknob has a really big potential to be the medium to spread the virus because the same surface is used by several people. Speech recognition can be used to solve this problem. In this study, Mel Frequency Cepstral Coefficients (MFCC) and Convolutional Neural Network (CNN) are going to be used as the extraction feature and classification method, respectively. We classify the sound signal into two classes (“buka” and “tutup”). People who want to open or close the door just need to say a specific command. This can be helpful to minimize the risk of COVID transmission. A CNN model is developed and fed with an audio file from a curated dataset for training and testing. With this system, we have successfully trained the model with an accuracy of 89% using an epoch of 50 and batch size of 32 as the parameters with a dataset distribution of 8:2 for training and validation. We believe this study will be influential in developing automated door systems using speech recognition, especially in the Indonesian language.

Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning.

The elevator door system plays a crucial role in ensuring elevator safety. Fault prediction is an invaluable tool for accident prevention. By analyzing the sound signals generated during operation, such as component wear and tear, the fault of the system can be accurately determined. This study proposes a GNN-LSTM-BDANN deep learning model to account for variations in elevator operating environments and sound signal acquisition methods. The proposed model utilizes the historical sound data from other elevators to predict the remaining useful life (RUL) of the target elevator door system. Firstly, the opening and closing sounds of other elevators is collected, followed by the extraction of relevant sound signal characteristics including A-weighted sound pressure level, loudness, sharpness, and roughness. These features are then transformed into graph data with geometric structure representation. Subsequently, the Graph Neural Networks (GNN) and long short-term memory networks (LSTM) are employed to extract deeper features from the data. Finally, transfer learning based on the improved Bhattacharyya Distance domain adversarial neural network (BDANN) is utilized to transfer knowledge learned from historical sound data of other elevators to predict RUL for the target elevator door system effectively. Experimental results demonstrate that the proposed method can successfully predict potential failure timeframes for different elevator door systems.

Full-scale experimental simulation of the stack effect pressure acting on an elevator door system in a high-rise building

The pressure-bearing performance of an elevator system under the stack effect in a super high-rise building is usually difficult to simulate directly in a laboratory environment due to the difficulty in fully meeting the similarity requirements. In this paper, a new full-scale experimental device for simulating stack pressure was designed in a large boundary layer wind tunnel laboratory, which was based on using wind pressure to simulate the action of thermal pressure on an elevator door system. By employing this approach, the pressure-bearing performances of an elevator door system under different levels of wind pressure of the static and dynamic conditions were systematically investigated, and the malfunction of the elevator door system due to the strong stack effect was successfully simulated. The critical pressure threshold of the elevator door system, which was difficult to evaluate accurately in previous studies, was then measured and it was approximately equal to 150 Pa. Meanwhile, the pressure-bearing state of the elevator door system was numerically simulated and compared with the wind tunnel experiment results. The study shows that the simulation employing the dynamic mesh model can accurately reflect the force characteristics of an elevator lobby door during its physical process of opening and closing. Finally, based on the experimental simulation results, the pressure-bearing thresholds of the elevator door systems of several typical high-rise buildings were estimated, and the reasonability of the full-scale simulation was finally verified by comparisons with field measurement data.

Intelligent door unlock system using AI MediaPipe

Prioritizing advanced security, the infusion of AI Mediapipe into the realm of smart door access not only simplifies access control but also guarantees unmatched protection, reshaping the expectation for safety and reliability. Implementing AI Mediapipe facilitates real-time gesture recognition, face detection, and hand tracking, optimizing access control in smart door systems. Integrating software and hardware set a new standard in the domain of smart door access. Here, we have proposed a smart system with a high-resolution camera to capture faces and connect them to a processing unit, i.e., Raspberry Pi. A face detection algorithm has been deployed into the hardware device so that the lock would open once an authorized face is detected. Our proposed system can make accurate predictions compared to existing systems. Integrating advanced computer vision and authentication methods marks a significant stride in the evolution of intelligent entry solutions.

Optimalisasi Perencanaan Dan Pengendalian Produksi Rak Palet Menggunakan Metode Cutting Plane

PT Atlantic Anugrah Metalindo is a manufacturing company that produces costumize products, steel door systems and fireproof doors. Currently PT Atlantic Anugrah Metalindo is producing 100 units of pallet rack manufacturing with customers from PT X so that proper material requirements planning is needed. The purpose of this research is to determine the material requirements of pallet racks optimally and efficiently. This research requires calculating the Bill of Materials (BOM) using the Cutting plane method and controlling the production process to run effectively and efficiently. Based on the calculations that have been carried out, the results show that for 2 mm thick mild steel material requirements, 6 sheets of plate are needed, 3 mm thick mild steel requires 173 sheets of plate, 3.6 mm thick mild steel requires 12 sheets of plate and 5 mm thick mild steel requires 12 sheets of plate. So that from these calculations PT Atlantic Anugrah Metalindo is expected to minimize the material requirements of pallet rack products.

A Fault Detection Approach Based on One-Sided Domain Adaptation and Generative Adversarial Networks for Railway Door Systems.

Fault detection using the domain adaptation technique is one of the more promising methods of solving the domain shift problem, and has therefore been intensively investigated in recent years. However, the domain adaptation method still has elements of impracticality: firstly, domain-specific decision boundaries are not taken into consideration, which often results in poor performance near the class boundary; and secondly, information on the source domain needs to be exploited with priority over information on the target domain, as the source domain can provide a rich dataset. Thus, the real-world implementations of this approach are still scarce. In order to address these issues, a novel fault detection approach based on one-sided domain adaptation for real-world railway door systems is proposed. An anomaly detector created using label-rich source domain data is used to generate distinctive source latent features, and the target domain features are then aligned toward the source latent features in a one-sided way. The performance and sensitivity analyses show that the proposed method is more accurate than alternative methods, with an F1 score of 97.9%, and is the most robust against variation in the input features. The proposed method also bridges the gap between theoretical domain adaptation research and tangible industrial applications. Furthermore, the proposed approach can be applied to conventional railway components and various electro-mechanical actuators. This is because the motor current signals used in this study are primarily obtained from the controller or motor drive, which eliminates the need for extra sensors.

Numerical investigation on mechanical behavior of door systems during seismic excitation

The opening and closing of doors in buildings likely become challenging when earthquakes occur owing to the deformation of the door frames. Although this may delay evacuation, there are no anti-seismic standards for doors defined in the Japanese Building Standards Act, which was amended in 1981. Discussions on doors or door frames from a structural point of view are still rare, even though the behaviors of doors, door frames, and hinges under seismic excitation are critical for avoiding secondary disasters after earthquakes. In this study, a seismic response analysis of a building model with door frames was performed using the adaptively shifted integration (ASI)-Gauss code to find an approach to reduce the deformation of door frames. Numerical results confirmed that the severe damage to the non-structural wall caused by the interlayer deformation of the building allowed both door frames and doors to be severely deformed. The in-plane deformation angles exceeded the angles causing difficulty in opening the doors. A door acted as an element resisting the door frame from deforming on the upper floors, and the door function would be lost on the lower floors by deforming in the out-of-plane direction. Furthermore, the overall deformations of the door frames decreased significantly even on the lower floors, where the interlayer drift angle of the building was larger, by placing the door system in a setback position. This effect was greater when the setback length was longer.

An Investigative Study of Voice Functioned Smart Door Lock System

This article discusses how speech recognition can improve security for persons and property while making door systems more accessible to people with impairments. Facial recognition, fingerprint scanning, and iris scanning are examples of popular biometric technologies. Based on features and qualities used to identify various people for the safety and security of their lives and property, these biometric identifiers are distinctive and one-of a-kind. Sadly, these biometrics are vulnerable to hacking. A pin or password can be cracked, a person's finger can be severed to perform a fingerprint scan, an eyeball can be removed to perform an iris scan, and a person's photo can be used to perform facial recognition. With the help of speech recognition biometrics technology, these obstacles can be reduced. Technology for voice biometrics is more precise, swifter, more practical. In order to give individuals a quick way to open their doors and simultaneously protect their safety and security, this research study intends to design a door access control system that makes use of voice recognition algorithms. The testing phase and the training phase are the two phases that make up the system. The attributes of a speech are extracted and stored in a database during the training phase. Using voice recognition algorithms and vocal models, the intents from a person's address would be derived during the testing phase. A user is given access if a match is detected.

Real-Time Prognostics and Health Management Without Run-to-Failure Data on Railway Assets

Prognosis is a challenging technology that aims to accurately predict and estimate the remaining useful life of a component or system in order to enhance its reliability and performance. Although prognosis research for predictive maintenance is a well-researched topic, practical examples of successful prognostic applications remain scarce. This is due to the lack of available run-to-failure data to build the prediction model as maintenance is usually conducted regularly to avoid significant defects. This paper proposes a novel prognosis method that can be applied to real-world railway maintenance planning without employing run-to-failure data. The key idea is that the fault severity assessment and approximate remaining time prediction are often all that is needed in order to plan maintenance. Firstly, using motor current signals, a degradation indicator on railway door systems is generated based on the dynamic time warping method to measure similarity between typical normal and faulty behaviour. Then, the K-means algorithm is applied to assess fault severity, followed by the representative time estimation for each level of fault severity. This estimation thus allows the remaining time prediction until reaching the critical fault severity level without using run-to-failure data. As a result, the proposed method enables predictive maintenance planning for railway door systems. In addition, the fault severity threshold can be updated by additional operational data, enabling the remaining time prediction to be more reliable. Furthermore, the proposed method can be applied to conventional railway assets and other electro-mechanical actuators as motor current signals are primarily available from the controller or motor drive without additional sensors.

A Subway Sliding Plug Door System Health State Adaptive Assessment Method Based on Interval Intelligent Recognition of Rotational Speed Operation Data Curve

The subway sliding plug door system is crucial for ensuring normal operation. Due to the differences in the structure and motor control procedures of different sliding plug door systems, the rotational speed monitoring data curves show great differences. It is a challenging problem to recognize the intervals of complex data curves, which fundamentally affect the sensitivity of feature extraction and the prediction of an assessment model. Aiming at the problem, a subway sliding plug door system health state adaptive assessment method is proposed based on interval intelligent recognition of rotational speed operation data curve. In the proposed method, firstly, the rotational speed operation data curve is adaptively divided by a long short-term memory (LSTM) neural network into four intervals, according to the motion characteristics of the door system. Secondly, the sensitive features of the door system are screened out by the random forest (RF) algorithm. Finally, the health state of the door system is assessed using the adaptive boosting (AdaBoost) classifier. The proposed method is comprehensively verified by the benchmark experiment data set. The results show that the average diagnostic accuracy of the method on multiple bench doors can reach 98.15%. The wider application scope and the higher state classification accuracy indicate that the proposed method has important engineering value and theoretical significance for the health management of subway sliding plug door systems.

A Fault Detection Technique based on Deep Transfer Learning from Experimental Linear Actuator to Real-World Railway Door Systems

Fault detection for railway door systems based on data-driven approaches has been investigated in recent years due to the massive amount of available monitoring data. Despite much attention to its application, the major challenge is the lack of available faulty datasets to build a reliable model since railway maintenance is usually conducted regularly to avoid significant defects from economic and safety points of view. We aimed to tackle the issue by employing transfer learning. Firstly, we built a long-short term memory-based deep learning model using linear actuator experimental datasets. Then, we employed a transfer learning technique to adjust the deep learning model to be available to real-world railway door systems using a small amount of faulty data. As a result, high fault detection accuracy can be obtained at 0.979 as F1 score. The result reveals that an accurate fault detection model can be built even though a large amount of labelled datasets is unavailable. In addition, the proposed method is applicable to other door systems or electro-mechanical actuators since the method is unspecific to physical mechanisms and fault modes, and the only motor current signal is used in this research. The signal is primarily available from the controller or motor drive without additional sensors.

Energy benchmarking analysis of subway station with platform screen door system in China

The energy consumption of Ventilation and Air Conditioning (VAC) systems in subway stations has attracted much attention, aiming to figure out energy-saving potentials and improve energy efficiency. Benchmarking is a useful method to evaluate energy performance of VAC systems during the operational stage. This study describes the development of an energy benchmarking model for the analysis of VAC energy consumption in subway stations with platform screen door systems in a variety of climate regions in China. Firstly, the Constraint Value (CV) and Leading Value (LV) of VAC energy use were simulated using a validated theoretical energy model, which represent the “general” and “superior” level of the VAC energy use, respectively. Results show that, for the 2,520 simulated cases, CV ranges from 184 to 859 × 103 kWh/year and LV ranges from 178 to 529 × 103 kWh/year, with different ranges for cold, hot-summer cold-winter, and hot-summer warm-winter region. Next, the simulated values of CV and LV were applied to establish an energy benchmarking model using the ordinary least square approach. For each climate region, the regressive energy benchmarking models for CV and LV were established and validated by cross-validation. Results show that R2 are 0.753–0.903 and MBE of the test set are 2.1–7.1%, indicating a good fit for the regressive benchmarking models. In order to further test their application, the regressive models were then applied to existing subway stations. Finally, the impacts of service-related variables, i.e. number of passengers, train departure density, and tunnel length, on the CV and LV were discussed. The proposed energy benchmarking model and analysis provide a convenient and effective tool for operators and managers to evaluate VAC energy consumption status and assess energy-saving potentials for subway stations.

Conceptual arguments on the use of unlatched reverse swing “flappy” door smoke control systems in residential buildings

PurposeThe proposed use of unlatched, reverse swing flappy doors is becoming widespread in the design of residential common corridor smoke control systems. This article explores the conceptual arguments for and against the use of these systems.Design/methodology/approachThis article relies on industry experience, with reference to relevant building design practices, standards and research literature, to categorise arguments. These are collated into four common areas of concern relating to compartmentation, reliability, depressurisation and modelling practices. A final comparison is made between different common corridor smoke control system types for these four areas.FindingsThe article highlights several concerns around the use of flappy door systems, including the enforced breaches in stair compartmentation, uncertainties around system reliability, the reliance on door closers as a single point of failure, the impact of day-to-day building use on the system performance and the false confidence that modelling assessments can provide in demonstrating adequacy. The article concludes in suggesting that alternative smoke control options be considered in place of flappy door systems.Originality/valueDiscussion on the use of flappy door smoke control systems has been ongoing within the fire engineering community for several years, but there is limited public literature available on the topic. By collating the common arguments relating to these systems into a single article, a better understanding of their benefits and pitfalls has been provided for consideration by building design and construction professionals.

Face Mask Detection Using Lightweight Deep Learning Architecture and Raspberry Pi Hardware: An Approach to Reduce Risk of Coronavirus Spread While Entrance to Indoor Spaces

The COVID-19 pandemic continues to spread around the world at full speed, threatening public health. In response, the World Health Organization recommends various preventive measures to reduce the spread of the COVID-19 virus. Wearing a mask is one of the preventive measures to reduce the contagion of the disease, and many governments around the world advise people to wear masks. One of the prominent symptoms of coronavirus is high fever. A person with a fever above normal is likely to have contracted the corona virus. This requires the identification of people with a high fever in order to prevent the epidemic in the public arena. This situation has caused people who want to enter public places to need masks and officers who control their body temperature. The aim of this study is to detect people who do not wear masks or do not wear them properly, and also to detect people with high fever through a system. The proposed system is designed as a system that can be integrated into automatic door systems. The system was basically implemented by running Mobile Net, one of the deep learning models, on the Raspberry Pi card. In the proposed method, 97.0% accuracy was obtained. Experimental results show that the proposed method can effectively recognize face masks and whether people have a high fever. This work is necessary for many closed areas that will make masks and fever control in public areas.

Smart Grounds

Abstract: Institutions like colleges and universities having grounds, indoor and outdoor stadiums, gyms. Maintaining and providing a good environment is a major task for institutions. To make them easy we use IoT and mobile applications. In this project, we use IoT to control the electrical devices in the grounds, stadium, and gyms. We control all the equipment with mobile applications. Like controlling the lights and ACs in the gym and indoor stadiums then accessing fed lights and water sprinklers in the grounds and automatic door systems. Maintaining the records of the individual who is accessing the gyms and stadium. The user will get the stats of the equipment which is assessed by them like, If the person is using the treadmill they will get the details stats of total time spent, total distance covered and speed, etc within the application. For providing these details we use check-in and check-out in the equipment. We update the events that going to be conducted on the campus and the detailed status of the event that's completed. User can prebook grounds to conduct events. Keywords: Mobile Application, Flutter, IoT, Google Firebase, Google Authentication, Google FireStore.

A Real-Time Fault Detection Framework Based on Unsupervised Deep Learning for Prognostics and Health Management of Railway Assets

Fault detection based on deep learning has been intensively investigated in the recent decade due to increasing availability of data and its ability to engineer features with deep neural network architectures. Despite much attention to its application, the major challenge is the lack of available labelled datasets to build the models since maintenance is usually conducted regularly to avoid significant defects. This paper aims to propose a successful real-time fault detection framework based on unsupervised deep learning using only healthy normal data. The approach is based on autoencoder architecture and a one-class support vector machine as a classifier. As a case study, large real-world datasets acquired from railway door systems have been employed. The five different types of deep learning models and a one-class classifier are trained and comprehensively validated based on performance metrics and sensitivity analysis. In addition, two experiments have been carried out to verify the model’s adaptability and robustness to variational time-series data. The result shows a typical autoencoder is the least sensitive to a decision boundary set by the one-class classifier. However, the two experiments show that the fault detection accuracy for a bidirectional long short-term memory-based autoencoder is considerably higher than other autoencoder-based models at 0.970 and 0.966 as F1 score, meaning only this model is adaptable and robust to variational data. The experimental result allows us to obtain the understandability of the deep learning models. Furthermore, the regions of anomalies are localised with unsupervised models, which enables diagnosing the cause of failure.

Reliability Analysis of Metro Door System Based on Fuzzy Multi-State Bayesian Network

Considering the shortcomings of the fault tree analysis (FTA) method in the reliability analysis of metro door systems, Bayesian network (BN) and fuzzy theory were introduced to establish the failure probability model of a metro door system. A fault tree of the metro door system was established based on the structure of the metro door, the operation data record and the practical experience of relevant engineers. The BN of the metro door system was constructed based on the fault tree. For the problem that the prior probabilities of root nodes with missing data were unavailable, fuzzy theory was introduced to convert the expert language values on these missing data nodes to corresponding prior probabilities, which were substituted into the BN along with the root nodes whose prior probabilities were obtained from the operation fault data to calculate the leaf node probability. Cause analysis of the metro door system was performed with bi-directional reasoning of BN, which provided a way to find the key factors that caused door faults and the metro door system fault probabilities.

Voice Recognition System for Door Access Control Using Mobile Phone

Security is one of the most important issues for any individual or organization, and as technology has advanced, numerous techniques to protecting lives and property have been deployed through door access control systems. The typical method of unlocking a door is to open it with a real key or by twisting the door knob. Physical keys that are used to open doors are subject to duplication and can be misplaced. Furthermore, typical biometric technologies and other technologies are vulnerable to a variety of failures, such as a person's finger being cut off to produce a fingerprint scan, a pin being hacked using various methods or permutations, and a person's photo being used for facial recognition. Furthermore, it is more difficult for people with physical disabilities to unlock a door system without the assistance or support of others. For example, it is difficult for a person in a wheelchair to open a door system without the assistance or support of another person. As a result, a speech recognition access control system that can accommodate both able-bodied and impaired people is unavoidable. This paper demonstrates how voice recognition may be used to access door systems by creating a door access control system that employs speech recognition to simplify the work of providing access to door systems via a mobile phone connected via Bluetooth. The system's performance is perfectly in line with its design.

CONCEPT DEVELOPMENT PROCESS FOR DOORS OF AUTONOMOUS VEHICLES

AbstractVehicle doors have barely changed in recent decades, and nor has the car. Since autonomous driving will lead to changes in vehicles and how they are used, their doors will also have to be rethought. In the project UNICARagil, researchers from several universities in Germany design and build four prototypes of driverless and autonomous vehicles, which are developed based on a new and modular architecture. As part of this, we developed a concept including a prototype of an automated door system. In this paper, we present our concept development process adapted for door systems of autonomous vehicles. Based on the vehicle concept development process, it should help researchers and engineers to select and design new door concepts in an early phase. At the end, by means of an example, we present the prototype of our door concept as well as a boarding user study we carried out. This study helps evaluate and improve the boarding comfort of future door concepts.