Modal Analysis Method Research Articles

PPE Education to Reduce the Risk of Workplace Accidents in The Furniture Industry

Occupational safety and health is an important aspect in the industrial world, including the furniture industry. If a company implements the Occupational Health and Safety well, it can reduce the risk of work accidents, thereby increasing work productivity. Work accidents can occur due to workers’ negligence or suboptimal implementation of the Occupational Health and Safety System. To reduce the risk of work accidents, a risk analysis is carried out, which is one part of risk management. One of the risk analyses that can be done is by using the Failure Mode and Effect Analysis (FMEA) method. Risk identification in the furniture industry is based on field reviews, which then assess the severity (S), occurrence rate (O), and detection level (D). Then the assessment results are sorted to determine the highest Risk Priority Number (RPN) value variable. In the furniture production process, there are various potential hazards, with the highest RPN found in the cutting process, where a machine incident occurred with an RPN value of 120. The severity factor is given a value of 6 because the error leads to torn injuries and equipment damage, so it must be handled immediately, and there is the repair of the damage. One effort to reduce the risk of work accidents is to provide PPE at work.

Risk Analysis of Contractor Selection in Construction Work Procurement

This research is about the risk analysis of the selection of contractors in the procurement of construction works. The method used to carry out the existing risk analysis is the Failure Mode and Effect Analysis method. The research was conducted on five Work Units for Procurement of Goods and Services (UKPBJ) with the highest number of failed tenders from 2021 to 2022. Risk identification was carried out by means of literature studies and pilot surveys in the form of interviews with several selection working groups. It was found that there were 26 risks to be analyzed. Data collection was carried out by distributing questionnaires to the selection working group, where the selection working group was asked to fill in the levels of severity, occurrence, and detection for the 26 existing risks. Then analyzed using the RPN method for each risk. The results obtained show that of the 26 existing risks, there are 10 that have an RPN value above the critical value. These risks are delays in procurement preparation documents from PPK SKPD, failed auctions, qualification results that are not the actual ability of participants, shortage of uploading tender documents, goods and services providers not interested in participating in the auction, objections or complaints from bidders, misunderstanding of information in the auction, Details of HPS known by bidders, Intimidation from Providers, NGOs, and law enforcement officials, and Mistakes in choosing a provider Then the level of importance of the risk is also determined by multiplying the frequency with the impact so that there is one risk that is at a moderate level, namely the risk of delay in procurement preparation documents from PPK SKPD. For the ten risks that are above the critical value, mitigation measures are carried out in order to minimize the impact that occurs.

Risk Assessment on The Bandung Hilton Hotel Construction Project Utilizing The FMEA and FTA Methods

Risk is the possibility or uncertainty of unexpected events occurring during the implementation of work which could harm the party carrying out the activity, the example is a risk of work accidents. The potential risk of work accidents also occurs in the Hilton Hotel Construction Project in Kota Baru Parahyangan - West Bandung. The project is a high-rise building with a height of 23 floors which of course has a fairly high risk of work accidents. This research aims to find out the dominant risk and its factors.This research applied two methods in analyzing, those methods are Failure Mode and Effect Analysis Method (FMEA) and Fault Tree Analysis Method (FTA). Having can be identified and analyzed that risks by using those methods, this research did some steps by using Method for Obtain Cut Set (MOCUS) for finding basic event combination.The results of this research are to determine the dominant risk of work accidents using the FMEA method, namely the activity of workers falling from a height during glass installation work with an RPN value of 26,3. Based on the FTA method, it produces 16 basic events are feeling unwell, lack of concentration, lack of enthusiasm for work, excessive joking, not seeing signs, lack of communication, lack of supervision time, lack of K3 training, less comfortable using PPE, limited amount of PPE, lack of training, lack of experience, signs that are too small, signs blocked by objects, slippery roads due to rain, and a messy work environment.

Improved calculation method for dry modal analysis of four-stage centrifugal-pump rotor system based on concentrated-mass method

Improved calculation method for dry modal analysis of four-stage centrifugal-pump rotor system based on concentrated-mass method

Koopman mode analysis on discovering distributed energy transfer of post-transient flutter of a bluff body

The flutter response of a rectangular cylinder model with an aspect ratio of 5 is investigated by direct numerical simulation for various Reynolds numbers (Re) and flow angles of attack (α). The results show that the lower α and Re, the larger the torsional amplitude at the critical reduced flow velocity (Ur) corresponding to the occurrence of flutter. High-order Koopman mode analysis method (HOKM) was developed to synchronously capture the inherent flow and structural modes. The element-mode-based energy transfer between cylinders and flow modes was used to reveal the underlying mechanism. HOKM analysis shows that only odd-order modes contribute to the occurrence of flutter, with an augmentation in Ur enhancing this phenomenon. By analyzing the work done in odd-order modes, it is found that the primary mode always occupies the main contribution to the work done. The leading-edge endpoint portion is always where the maximum work is done. An escalation in Re and α notably influences both the spatially relevant part (WΦ) and the temporally relevant part (Wc), while an increase in Ur predominantly impacts WΦ. This study offers universal guidance on the safety and protection of ocean engineering structures and flow control strategies.

Time analysis for aero-engine acoustic modes exploiting block sparsity

Acoustic Mode Analysis (AMA) for aero-engines can offer valuable insights for the design of silent engines as well as for fault diagnosis. Commonly, this is done in the (spatial) Fourier domain, necessitating the use of multiple uniformly spaced microphones to ensure adequate resolution. Recent works show that sub-Nyquist estimation is feasible using sparse reconstruction frameworks, although such modelling generally introduces an estimation bias that has to be compensated for. Moreover, there is a growing interest in monitoring mode amplitude over continuous time, as it can offer crucial insights for diagnosing operational conditions. In this work, we introduce a Block Orthogonal Matching Pursuit (BOMP) method for continuous time mode analysis, exploiting the underlying structural sparsity of the signal model. Specifically, the (pseudo) ℓ0−norm penalty is employed to induce sparsity in the wavenumber domain, whereas a block structure is imposed as a constraint to monitor the amplitude variation in the time domain. The effectiveness of the BOMP is evaluated using both numerical simulations and experimental measurements, indicating the proposed method’s preferable performance as compared to the classic Least Absolute Shrinkage and Selection Operator (LASSO) and Orthogonal Matching Pursuit (OMP) methods.

Feasibility of a localized mode analysis method in an SOI platform based on carrier grating

In order to measure the intensity of modes that are transmitted inside the devices on the silicon-on-insulator (SOI) platform, researchers usually use pre-processed couplers to make the optical modes diffract out of the chip. However, the output couplers have an influence (e.g., attenuation and wavelength selectivity) on the modes of concern. Besides, as the quantity and variety of devices integrated into the SOI platform continue to escalate, the traditional method also shows limits on detecting devices far from the chip edge. So, is it feasible to directly and locally measure one specific mode’s intensity on some waveguide-based devices like the directional coupler, polarization beam splitter, and so on. Interference of two coherent pump beams has the capability to induce a periodic carrier distribution in the material, thus modulating the refractive index, effectively creating a temporary and erasable diffraction grating. In this study, an off-chip, non-destructive, and localized detection method based on carrier grating is proposed. A theoretical model is developed to calculate carrier dynamics under various pump configurations. Leveraging the finite-difference time-domain (FDTD) method and accounting for free carrier index (FCI) and free carrier absorption (FCA) effects, analysis of the quantitative impact of pump intensity and radius on the diffraction efficiency of the carrier grating in the silicon-on-insulator (SOI) platform and its far-field divergence characteristics is provided. Ultimately, this research contributes to a discussion on several commonly used application scenarios and the feasibility of experimental approaches. A spatial resolution of less than 10 µm and a diffraction efficiency of −15dB while simultaneously maintaining a far-field divergence of 7.8° for the SOI platform are proposed at the end of this article.

Identification and Overcome the Harmful Situations Related to Chemical with the Help of Hazard Identification and Risk Assessment Using Failure Mode and Effective Analysis (FMEA) Method

Exposure of Chemical are a major occupational health and safety issue in chemical work place industry. Management of chemical hazards requires the combined efforts of occupational health and safety specialists, including generalist OHS professionals, occupational hygienists, and occupational health practitioners. This Research is about analysis of Hazards and their developed Control Measures in industrial chemicals, the manner in which their toxicity is assessed and the use of such assessments in regulatory decision-making. It begins with general points concerning toxicological data availability and hazard identification, then moves on to risk assessment and occupational exposure limits, and finally looks briefly at some standard specific toxicological issues, where the science is far from resolved after brief consideration of the historical context of chemical reactivity and toxicity issues, acute and chronic exposure, chemical hazard classification systems, and the identification, risk assessment, and control of chemical hazards

Finite element numerical analysis of structural vibration of a certain pump station

Abstract In the process of pump station operation, the vibration of factory structure caused by hydraulic or mechanical factors will seriously threaten the ordinary operation of pump station; reduce the efficiency of units; shorten the service lifespan of equipment, and even lead to failure of units. In this paper, ANSYS software and modal analysis method are applied to simulate the vibration effect on pump station under natural vibration, unit vibration excitation and hydraulic excitation. The intrinsic vibration features of pump station structure under diverse boundary conditions, different working conditions and distinct external excitation sources are studied. According to the results of modal and harmonic response analysis, it can be concluded that: 1. A cross relationship has been found existing between pump station structure and unit excitation of speed frequency. 2. In addition to the upper part of the pump station fragile structure, the vibration of the pier is more noteworthy. 3. The vibration amplitude of the whole pump station along vertical direction is larger than that in other directions while the deformation following the unit axis and flow direction presents a strong tendency. 4. In general, the influence of hydraulic load on the structural force of pump station is relatively small.

Genetic Causality between Type 1 Diabetes and Arrhythmia Identified by a Two-sample Mendelian Randomization Study

BackgroundClinical studies have shown that cardiovascular diseases in patients with type 1 diabetes (T1D) are often atypical or asymptomatic. The link between T1D and arrhythmia remains unclear. To infer causality between T1D and arrhythmia at the genetic level, we conducted a Mendelian randomization study through the genetic tools of T1D. MethodsIn this study, we used genetic variables and summary statistics from genome-wide association studies of T1D and arrhythmia. Single nucleotide polymorphisms were selected based on the assumptions of instrumental variables. The inverse variance-weighted method was used as the primary analysis to summarize the causal effects between exposure and outcome. The weighted median and weighted mode methods were used as secondary methods. We tested for horizontal pleiotropy using the MR-Egger method and detected heterogeneity using the Q-test. A leave-one-out sensitivity analysis was performed. Scatter plots, forest plots, and funnel plots were used to visualize the results of the MR analysis. ResultsIn this study, we selected 28 T1D-related SNPs as instrumental variables. The IVW [odds ratio (OR) = 0.98, 95 % confidence interval (CI) = 0.97−1.00, P = 0.008], weighted median (OR = 0.98, 95 % CI = 0.96 − 0.99, P = 0.009), and weighted mode (OR = 0.98, 95 % CI = 0.96−0.99, P = 0.018) analysis methods suggested a causal effect of T1D on arrhythmia. The MR-Egger method indicated no horizontal pleiotropy (P = 0.649), and the Q-test showed no heterogeneity (IVW, P = 0.653). ConclusionsOur MR analysis revealed a causal association between T1D and the development of arrhythmia, indicating that patients with T1D had a higher risk of arrhythmia.

Medication Error Prevention Using Healthcare Failure Mode And Effect Analysis At Clinical Pharmacy Installation

Medication error is the second most common patient safety incident worldwide. Medication errors can be defined as unintentional failures in medication services that have the potential to cause harm to patients. Maintaining safe health services is highly dependent on the ability of service providers to proactively conduct patient safety risk analysis, one of which is using the Healthcare Failure Mode and Effect Analysis (HFMEA) method. This study was aimed at obtaining the HFMEA design as an effort to prevent medication errors at Company X Clinical Pharmacy Installation. The research method used is qualitative research with a specific type of Operations Research. Data collected by in-depth interviews, observation, secondary data analysis, and focus group discussion. The results of this study found factors that cause medication errors to occur from organizational factors and staff factors. These results then analyzed for the HFMEA design which obtained 11 risks that required attention and then 14 action plans are made to overcome them. This study successfully developed HFMEA design to prevent medication error in Company X Clinical Pharmacy Installation.

A fast analysis algorithm for structural vibration modal sensitivity

Vibration modal sensitivity analysis has a wide range of applications in structural optimization design, model correction, fault detection, and reliability analysis. The existing methods for calculating modal sensitivity mainly include modal superposition method, Nelson’s method, and some improved algorithms derived from them. However, the existing algorithms have the drawback of low computational efficiency when applied to modal sensitivity analysis of the large-scale structures. In view of this, the objective of this work is to develop a fast modal sensitivity analysis method that can more efficiently calculate the modal sensitivity of the large structures with thousands of degrees of freedom. This new sensitivity analysis technique integrates Sherman-Morrison-Woodbury formula, subspace iteration and forward-difference to achieve the goal of fast calculation. The innovations of the proposed method mainly include the following aspects. Firstly, the Sherman-Morrison-Woodbury formula is used to quickly calculate the perturbed flexibility matrix due to the minor modification in the structure. Then, the perturbed flexibility matrix is introduced into the subspace iteration method to calculate the perturbed modal parameters. Finally, the forward-difference algorithm is used to calculate the modal sensitivity. Two numerical examples are used to verify the superiority of the proposed modal sensitivity algorithm. Taking the 1952-bar structure as an example, the calculation time of the proposed algorithm is only 8.3 % and 18.2 % of that of the existing approximate modal superposition method and the improved Nelson’s method, respectively. In terms of calculation accuracy, the results obtained by the proposed method are exactly the same as the exact solution when the perturbation scale is set to 10−5. It is found that the proposed method significantly reduces computation time compared to the existing methods on the premise that the calculation accuracy is basically unchanged.

Pengendalian Kualitas Produk Gula Studi Kasus PT Madubaru PG. PS. Madukismo Yogyakarta

PG Madukismo is a sugar factory that was established in 1955 and is the only sugar factory in the province of the Special Region of Yogyakarta. This factory is located in Padokan Village, Tirtonirmolo Village, Kasihan District, Bantul Regency, Special Region of Yogyakarta. PT Madubaru, a company engaged in sugar cane processing, is a sugar and spirits factory located in the Yogyakarta area. Companies that produce cane sugar with SHS IA quality. Product defects are often found, resulting in a decrease in quality, therefore it is necessary to carry out quality control. This study uses the Failure Mode and Effect Analysis (FMEA) method to identify failure modes, Logic Tree Analysis (LTA) to determine the consequences of failure arising from failure modes and determine effective preventive maintenance policies for each machine. For the production quality control section, the Statistical Processing Control (SPC) method is used to analyze product defects to determine whether product defects are still within controllable limits or not. While the Failure Mode and Effect Analysis (FMEA) method is used to determine the failure mode in sugar production. For the quality control section on raw materials using the Economic Order Quantity (EOQ), Re-Order Point (ROP), Safety Stock (SS), and Total Inventory Cost (TIC) methods. The conclusion of this study is regarding the results of the analysis and calculations carried out with several settlement methods and there are several causes of production defects from 5 factors, such as: human factors, work methods, environment, raw materials and machines used. And provide solutions to companies in order to reduce defects by controlling product quality.

Risk Analysis of Operational Disruptions in Public Electric Vehicle Charging Stations Using the Failure Mode and Effects Analysis (FMEA) Method

The Indonesia government is actively promoting the adoption of electric vehicles, as detailed in the 2021-2030 Electricity Supply Business Plan. The state-owned electricity provider, PLN, is responsible for establishing Public Electric Vehicle Charging Stations (PEVCS). However, several of these stations have encountered malfunctions; notably, 82 of the 567 stations are classified as Unavailable, indicating they are non-functional. Research literature points to a financial loss of $34,000 from operational issues at PEVCSs. This research aims to helps management understand and prioritize disruption that leads to failures or damages at these stations. Method used is the Failure Mode and Effects Analysis (FMEA) method along with logistic regression to examine the disruptions at PEVCSs labeled as Unavailable. The data for this research comes from a six-month historical record of PEVCS disruptions. The variables utilized for logistic regression analysis include foundational variables from the FMEA methodology—Severity, Occurrence, and Disturbance—complemented by two supplementary variables: the speed and age of the PEVCS. Result was found that three out of twelve types of disruptions have a high likelihood of failure, specifically issues with Device Communication, Connectivity, and Emergency Stop functions. A disruption is deemed likely to cause failure if its probability exceeds 50%.

Influence of Empirical and Dynamic Periods on the Seismic Responses of Reinforced Concrete Buildings Braced by L-Shaped Shear Walls

The Algerian Seismic Regulation of 1999, version 2003 (RPA99/v2003), presents ambiguities and a lack of thorough explanations in certain articles, which has led to a diversity of readings and interpretations among various stakeholders in the field, particularly the control offices. This situation has caused significant disagreements, especially during the approval of civil engineering files, thus resulting in a negative impact on project progress. To ensure adequate seismic protection of civil engineering structures, this work aims to unify the interpretations of certain passages of the regulation, with a particular emphasis on verifying the fundamental period of the structure, the shear forces at the base, and inter-story displacements using seismic calculation methods prescribed by the RPA. The comparative study demonstrates that no correlation between the period determined by the equivalent static method and that calculated by the spectral modal analysis method has been observed. This justifies the conclusion that adjusting between the empirical period and the dynamic period is not necessary.

A Novel Method for Failure Mode and Effect Analysis Based on the Fermatean Fuzzy Set and Bonferroni Mean Operator

Failure mode and effects analysis (FMEA) helps to identify the weak points in the processing, manufacturing, and assembly of products and plays an important role in improving product reliability. To address the shortcomings of the existing FMEA methods in terms of the uncertainty treatment of information and not considering the weights and correlations between risk factors, we propose a new FMEA method. In this paper, the Fermatean fuzzy Z-number (FFZN) is proposed by fusing the Fermatean fuzzy number and Z-number. Extending it to the Bonferroni mean (BM) operator, the Fermatean fuzzy Z-number-weighted Bonferroni mean (FFZWBM) operator is proposed. A new FMEA method is proposed based on this operator. In order to overcome the factors not considered in the FMEA method, two new risk factors are proposed and added. The ability of experts to express fuzzy information is enhanced by introducing the FFS. The weights and correlations between the influencing factors can be handled by aggregating the evaluation information using the FFZWBM operator. Finally, the proposed method is applied to an arithmetic example and the accuracy of the proposed method is proved by teaming it with other methods.

High-gain TM11 mode equilateral triangular patch antenna with shorting pins and triangular short horn

Abstract Normally, the reported gain of the microstrip patch antenna is within 8 dBi. Using properly located three shorting pins on three bisectors, the present work reports a method to convert the non-radiating TM11 mode of equilateral triangular patch antennas (ETPAs) to a deformed TM11 radiating mode. The boresight gain of ETPA operating in TM11 mode is enhanced from −10.75 to 12.1 dBi at 5.43 GHz. The boresight measured gain is further enhanced to 14.2 dBi at 5.52 GHz by using a triangular surface-mounted short horn (SMSH) of about ${{\lambda }}/5$ height. The aperture efficiency of the ETPA with the shorting pins is 84.2%. The aperture efficiency is further improved to 94.2% using the SMSH. The measured boresight cross-polarization and side-lobe level are −40 and −29 dB, respectively. The nature of the electricfield and surface current distribution is analyzed, using both the characteristic mode analysis method and high-frequency structure simulator, to understand the role of shorting pin and coaxial feed in converting the non-radiating TM11 mode to the radiating mode. A systematic design process also is presented for a fast design of shorting pin-loaded ETPA on the suitable substrate at a specified frequency.

Analysis Method of Special Physical Training Mode of Basketball Teams in Colleges Based on WeChat Applet and FTTA Optimised LSTM

OBJECTIVE: this paper proposes a basketball special physical training mode analysis method based on WeChat applet and optimization algorithm to improve the deep learning network.METHODS: Using the applet data set and the coaches' record data as model input data, the proposed method is used to analyse and thus improve the performance of the basketball team's special physical training pattern.RESULTS: Comparing the analysis effect between FTTA-Attention-LSTM analysis model and LSTM, FTTA-LSTM, FTTA-GRU, FTTA-BiLSTM models, the WeChat mini-program oriented basketball team's special physical fitness training mode analysis index system contains 14 factors affecting the analysis model; in analysing the relationship between the size of FTTA population and Attention-LSTM network hidden layer node number experiments, it was found that the selection of the population size of 80, the number of hidden layer nodes for 90; by analysing the FTTA-Attention-LSTM analysis model and other comparative models, it was found that the analysis accuracy of the FTTA-Attention-LSTM analysis model is the smallest, and the analysis time meets the real-time requirements, controlled within 0.001s.CONCLUSION: In the future, principal component analysis technology can be introduced for feature selection to further achieve intelligence and improve the analysis efficiency of the model.

Utilizing data-driven algorithms for blind modal parameter identification of structures from output-only video measurements

In structural dynamics and vibration analysis, modal identification plays a pivotal role in understanding and characterizing the dynamic behavior of complex systems. Traditional modal analysis techniques heavily rely on accurate sensor placement and knowledge of the excitation forces, which may not always be feasible or practical in real-world scenarios. Recently, non-contact video-based modal analysis methods for structures with arbitrary complexity using computer vision techniques have gained much importance. But these methods need to know ahead of time where the structure's natural frequency ranges are, and they use steerable pyramids, which are complicated multi-scale image decomposition filters. To address these issues, a blind modal identification technique is suggested to extract the modal parameters (modal frequency and mode shapes) from the recorded structural vibration video signal. The proposed algorithm for unsupervised machine learning is the Non-Negative Matrix Factorization (NNMF) method, which is combined with the Generalized Complexity Pursuit (GCP) method for blind source separation. The NNMF algorithm is directly applied to the raw pixel-time series made from the video data to get the temporal components, which are then separated using GCP to find the individual modal frequencies and mode shapes. The above algorithm is first validated on a numerical model and then implemented on laboratory scale models (cantilever, single-degree, and multi-degree frame models). Finally, the proposed methodology is implemented on real-world recorded structural vibration videos to determine its noise sensitivity, such as the Tacoma Narrows bridge and the London Millennium footbridge. The modal parameters extracted are compared with those from the available literature for validation. The estimation errors obtained from all the validations are well below 1%, which makes the technique quite suitable and reliable for structural vibration monitoring by identifying and reproducing complex vibration modes.

Multi-type classification and 3D visualization of internal defects in concrete plates based on a hybrid method of deep learning algorithm and bending mode analysis for acoustic vibration signals

In this paper, a hybrid method combining the deep learning algorithm and bending mode analysis of acoustic vibration signals is proposed for the multi-type classification and 3D visualization of internal defects in concrete plates. A novel deep learning model termed fully convolutional network based on principal component analysis and attention-embedded long-short term memory (PCA-ALSTM-FCN) is established. The PCA-ALSTM-FCN model successfully classifies multi-type internal defects, and the two-dimensional (2D) defect contour maps are generated based on the predicted state by the trained model. The defect depths are calculated according to the analytical formula of bending vibration mode of acoustic vibration signals. Combining the 2D defect map and depth information, the three-dimensional (3D) visualization images of defects are obtained. The average recognition accuracy of PCA-ALSTM-FCN model for different types of defects reaches 94.8 %, and the defect depth calculation error range is approximately 10 %–20 %. The experimental results show that the proposed method in this paper can accurately distinguish different types of internal defects and effectively locate the 3D position of defects in concrete.