Lifetime Prediction of a Hydraulic Pump Using ARIMA Model

Sharma, Anubhav Kumar; Punj, Pratik; Kumar, Niranjan; Das, Alok Kumar; Kumar, Ajit

doi:10.1007/s13369-023-07976-6

Cited by 10 publications

(3 citation statements)

References 36 publications

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“…At this stage, the human resource elasticity manager should not be concerned with other rooms in hospitals or the city but should only determine how many attendants are necessary to satisfy his own future demand for care. To this end, a time series is generated for the number of patients arriving at each time point and for the service time to input an ARIMA prediction model [43]. Consequently, when our model detects waiting times that are not in accordance with established limits, HealCity must calculate the amount of health resources required to meet the patient's demand, thus recognizing the need for adjustments in that particular room.…”

Section: Room-level Proactive Elasticitymentioning

confidence: 99%

Multi-Hospital Management: Combining Vital Signs IoT Data and the Elasticity Technique to Support Healthcare 4.0

Fischer,

Ramos,

Costa

et al. 2024

IoT

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Smart cities can improve the quality of life of citizens by optimizing the utilization of resources. In an IoT-connected environment, people’s health can be constantly monitored, which can help identify medical problems before they become serious. However, overcrowded hospitals can lead to long waiting times for patients to receive treatment. The literature presents alternatives to address this problem by adjusting care capacity to demand. However, there is still a need for a solution that can adjust human resources in multiple healthcare settings, which is the reality of cities. This work introduces HealCity, a smart-city-focused model that can monitor patients’ use of healthcare settings and adapt the allocation of health professionals to meet their needs. HealCity uses vital signs (IoT) data in prediction techniques to anticipate when the demand for a given environment will exceed its capacity and suggests actions to allocate health professionals accordingly. Additionally, we introduce the concept of multilevel proactive human resources elasticity in smart cities, thus managing human resources at different levels of a smart city. An algorithm is also devised to automatically manage and identify the appropriate hospital for a possible future patient. Furthermore, some IoT deployment considerations are presented based on a hardware implementation for the proposed model. HealCity was evaluated with four hospital settings and obtained promising results: Compared to hospitals with rigid professional allocations, it reduced waiting time for care by up to 87.62%.

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Section: Room-level Proactive Elasticitymentioning

confidence: 99%

Multi-Hospital Management: Combining Vital Signs IoT Data and the Elasticity Technique to Support Healthcare 4.0

Fischer,

Ramos,

Costa

et al. 2024

IoT

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“…As the estimate approach is only applicable to stationary series, ensuring that the series under consideration is stationary is the first and most crucial criterion for ARIMA modelling [34,35]. If neither a series' mean nor its autocorrelation change over time, it is said to be stationary [36,37]. Using a time plot tests, one must determine whether a timeseries is stationary.…”

Section: Modelmentioning

confidence: 99%

Enhancing Sustainable Dairy Industry Growth through Cold-Supply-Chain-Integrated Production Forecasting

Kashyap,

Shukla,

Jha

et al. 2023

Sustainability

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Cold supply chains (CSCs) are critical for preserving the quality and safety of perishable products like milk, which plays a vital role in the daily lives of a vast population, especially in countries like India. This research centers on sustainable milk production in Northern India, with priorities of ensuring efficiency and waste reduction within the cold supply chain. Leveraging data from a prominent North India-based dairy company, Company ‘X’, an ARIMA model is applied for predicting monthly milk production trends. Utilizing the Statistical Package for the Social Sciences (IBM SPSS STATISTICS 20) software, the study forecasts Company ‘X’s monthly milk production and identifies four distinct ARIMA models based on the autocorrelation function (ACF) and the partial autocorrelation function (PACF). By comparing predicted and actual milk production values (April–October 2021), sustainability metrics are integrated into ARIMA forecasts. Implications for the dairy sector’s sustainability and alignment with the Sustainable Development Goals (SDGs) are assessed through error terms such as R squared (R2) and mean absolute percentage error (MAPE). The study promotes sustainable milk production practices in Northern India’s dairy sector, resonating with the SDGs to optimize demand–supply dynamics and foster a more environmentally conscious dairy industry.

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“…Besides, the demand was autocorrelated [1]. Anubhav et al employed the ARIMA model to forecast the performance of hydraulic pumps and developed a technique for predicting the Remaining Useful Life (RUL) of these pumps, which also played a key role in repairing and maintaining off-road hydraulic vehicles [2].…”

Section: Introductionmentioning

confidence: 99%

A Case Study of Society General Securities based on ARIMA Model

Wang

2024

HBEM

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Stock price changes have always been a high concern for investors. Stock price modeling can indicate the stock changing trend, which can not only provide references for investors, but also can see the future development of the relevant market by analyzing the changing stock prices. Therefore, this paper establishes an Auto-Regressive Moving Average Model (ARIMA) of the opening price as well as the closing price of the trading day in Industrial Securities Co., Ltd. from November 2021 to August 2023 to predict the opening and closing price of the next seven periods of direction. The results show that the opening price will have a small fluctuation in the next 7 periods and the closing price will decline slowly. The goodness of fit of opening and closing prices are greater than 0.98, so this result is good. The result shows that this prediction of stock price by the ARIMA model is feasible and the result can be used as a basis for investors to predict future stock prices.

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Lifetime Prediction of a Hydraulic Pump Using ARIMA Model

Cited by 10 publications

References 36 publications

Multi-Hospital Management: Combining Vital Signs IoT Data and the Elasticity Technique to Support Healthcare 4.0

Multi-Hospital Management: Combining Vital Signs IoT Data and the Elasticity Technique to Support Healthcare 4.0

Enhancing Sustainable Dairy Industry Growth through Cold-Supply-Chain-Integrated Production Forecasting

A Case Study of Society General Securities based on ARIMA Model

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