An integrated machine learning: Utility theory framework for real-time predictive maintenance in pumping systems

2020 ◽  
pp. 095440542097051
Author(s):  
Raghad M Khorsheed ◽  
Omer Faruk Beyca
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Utility Theory ◽  
Predictive Maintenance ◽  
Manufacturing Operations ◽  
Pumping Stations ◽  
Bearing Failures ◽  
Temperature Levels ◽  
The Right

Bearings are the most widely used mechanical parts in rotating machinery under high load and high rotational speeds. Operating continuously under such harsh conditions, wear and failure are imminent. Developing defects give rise to even-higher vibration and temperature levels. In general, mechanical defects in a machine cause high vibration levels. Therefore, bearing fault identification and early detection enables the maintenance team to repair the problem before it triggers catastrophic failure in the bearing. Machine downtime is thus avoided or minimized. This paper explores the use of Machine Learning (ML) integrated with decision-making techniques to predict possible bearing failures and improve the overall manufacturing operations by applying the correct maintenance actions at the right time. The accuracy of the Predictive Maintenance (PdM) module has been tested on real industrial production datasets. The paper proposes an effective PdM methodology using different ML algorithms to detect failures before they happen and reduce pump downtime. The performance of the tested ML algorithms is based on five performance indicators: accuracy, precision, F-score, recall, and an area under curve (AUC). Experimental results revealed that all tested ML algorithms are successful and effective. Furthermore, decision making with utility theory has been employed to exploit the probability of failures and thus help to perform the appropriate maintenance interventions. This provides a logical framework for decision-makers to identify the optimum action with the maximum expected benefit. As a case study, the model is applied on forwarding pumping stations belonging to the Sewerage Treatment Company (STC), one of the largest sewage stations in Qatar.

scholarly journals Outside in: exile at home an algorithmic discrimination system

Artnodes ◽  
2020 ◽  
Author(s):  
Annabel Castro
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Present Article ◽  
Daily Life ◽  
Machine Learning Algorithms ◽  
Indefinite Detention ◽  
The Right ◽  
Japanese Ancestry ◽  
At Home

Outside-in is an installation that utilises machine learning to reflect on systematic discrimination by focusing on the indefinite detention of Mexicans with Japanese heritage concentrated in Morelos during WWII. This algorithmic discrimination system tears apart four classic fiction films continuously within a projection room. The fragments are displaced and classified using machine learning algorithms. The system selects, separates and reassembles the fragments into new orders. It evokes the condition of being robbed of your right to be in the place to which you belong. The citizens detained during WWII were removed from their residence, their belongings were confiscated and they were placed in seclusion solely for having Japanese ancestry. Similarly, at present, data retrieving companies configure low resolution representations of ourselves from the snatched digital debris of our daily life. These pieces are reconfigured into archetypes and meaning is attached to them for massive decision making. We don’t have the right or means to know what these representations look like or what meaning has been attached to such shapes. It is a privilege reserved to the designers of algorithmic processes: they own this right and we the citizens own the consequences. The present article is a case study presenting the creation of Outside in: exile at home as an installation that utilises machine learning and reflects on this kind of systematic discrimination.

scholarly journals Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1044
Author(s):  
Yassine Bouabdallaoui ◽  
Zoubeir Lafhaj ◽  
Pascal Yim ◽  
Laure Ducoulombier ◽  
Belkacem Bennadji
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Predictive Maintenance ◽  
Data Availability ◽  
Machine Learning Techniques ◽  
Automation System ◽  
Building Maintenance ◽  
Collection Data ◽  
Iot Devices

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

scholarly journals Outsourcing energy retrofitting of hotel buildings: the decision-making process

2019 ◽  
Vol 266 ◽  
pp. 01016 ◽  
Author(s):  
M.F.F. Fasna ◽  
Sachie Gunatilake
Keyword(s):  
Decision Making ◽  
Energy Performance ◽  
Decision Making Process ◽  
Existing Buildings ◽  
Energy Retrofit ◽  
Energy Efficiency Improvement ◽  
Adoption And Implementation ◽  
Energy Service ◽  
The Right

Poor energy performance of existing buildings worldwide has led to a crucial need to retrofit existing buildings to minimise energy consumption. Among the existing buildings, hotels use as much as 50% of their total expenses on energy and offer significant opportunities for energy efficiency improvement. Yet, comparatively the level of implementation of energy retrofits found to be low, which has attributed to, inter alia, the absence of a clearly defined process for ensuring the delivery of energy retrofit projects and lack of proactive guidance for project teams to ensure that they make the right decisions at the right time to achieve the desired outcomes. Since many energy retrofit projects in existing hotels are carried out with the involvement of an external contractor, or an Energy Service Company (ESCO), this study focuses on investigating the decision-making process in implementing energy retrofits when the project is outsourced to an external party. An in-depth case study is used to obtain insights into the critical decisions to be taken and key activities to be performed throughout the decision-making process. The findings are used to propose a step-by-step decision-making process comprising of three key phases: i.e., pre-retrofit, retrofit implementation and post-retrofit. It is hoped that the decision-making process developed in this study will serve as a roadmap for the effective adoption and implementation of energy retrofits in existing hotel buildings when an external contractor is involved.

Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology

2020 ◽  
Vol 120 (6) ◽  
pp. 1149-1174 ◽  
Author(s):  
K.H. Leung ◽  
Daniel Y. Mo ◽  
G.T.S. Ho ◽  
C.H. Wu ◽  
G.Q. Huang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Real Time ◽  
Service Providers ◽  
Series Data ◽  
Illustrative Case ◽  
Implementation Framework ◽  
Inference System ◽  
Content Type

PurposeAccurate prediction of order demand across omni-channel supply chains improves the management's decision-making ability at strategic, tactical and operational levels. The paper aims to develop a predictive methodology for forecasting near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better.Design/methodology/approachThe paper proposes a novel machine learning predictive methodology through the integration of the time series data characteristics into the development of an adaptive neuro-fuzzy inference system. A four-stage implementation framework is developed for enabling practitioners to apply the proposed model.FindingsA structured model evaluation framework is constructed for cross-validation of model performance. With the aid of an illustrative case study, forecasting evaluation reveals a high level of accuracy of the proposed machine learning approach in forecasting the arrivals of real e-commerce orders in three different retailers at three-hour intervals.Research limitations/implicationsResults from the case study suggest that real-time prediction of individual retailer's e-order arrival is crucial in order to maximize the value of e-order arrival prediction for daily operational decision-making.Originality/valueEarlier researchers examined supply chain demand, forecasting problem in a broader scope, particularly in dealing with the bullwhip effect. Prediction of real-time, hourly based order arrivals has been lacking. The paper fills this research gap by presenting a novel data-driven predictive methodology.

scholarly journals COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHM ON HOTEL REVIEW SENTIMENT ANALYSIS (CASE STUDY: LUMINOR HOTEL PECENONGAN)

2020 ◽  
Vol 16 (1) ◽  
pp. 59-64
Author(s):  
Jaja Miharja ◽  
Jordy Lasmana Putra ◽  
Nur Hadianto
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Business Decisions ◽  
Machine Learning Classification ◽  
Business People ◽  
Auc Value ◽  
The Right

Analysis of hotel review sentiment is very helpful to be used as a benchmark or reference for making hotel business decisions today. However, all the review information obtained must be processed first by using an algorithm. The purpose of this study is to compare the Classification Algorithm of Machine Learning to obtain information that has a better level of accuracy in the analysis of hotel reviews. The algorithm that will be used is k-NN (k-Nearest Neighbor) and NB (Naive Bayes). After doing the calculation, the following accuracy level is obtained: k-NN of 60,50% with an AUC value of 0.632 and NB of 85,25% with an AUC value of 0.658. These results can be determined by the right algorithm to assist in making accurate decisions by business people in the analysis of hotel reviews using the NB Algorithm.

scholarly journals AHP Method to Support Decision Making for Sustainability

2020 ◽  
Vol 13 (4) ◽  
pp. 32
Author(s):  
Wail El hilali ◽  
Abdellah El manouar ◽  
Mohammed Abdou Janati Idrissi
Keyword(s):  
Decision Making ◽  
Business Models ◽  
Multicriteria Decision Making ◽  
Ahp Method ◽  
Telecom Operator ◽  
Fierce Competition ◽  
The Right ◽  
Scarcity Of Resources ◽  
New Framework

In these challenging times, finding a way to sustain the created value becomes a must. The fierce competition, the risk of disruption, the rise of customer awareness and the scarcity of resources, all these are few of many drivers that push companies to invest in sustainability. This paper is an attempt to enrich the literature about this subject. It mainly explores how to use the AHP method, a well-known multicriteria decision making technique, to decide about the right actions to implement, in order to reach sustainability. The paper is a continuity of a previous work that introduced a new framework that explained how companies could sustain their business models through information systems (IS). This approach was applied on a telecom operator, as a case study, to explain well how companies could choose the right actions to implement, in order to reach sustainability.

Advantages, Limitations, and Solutions in the Use of ERP Systems

2013 ◽  
pp. 665-674
Author(s):  
Paula Serdeira Azevedo ◽  
Mário Romão ◽  
Efigénio Rebelo
Keyword(s):  
Decision Making ◽  
Enterprise Resource Planning ◽  
Hospitality Industry ◽  
Resource Planning ◽  
Erp Systems ◽  
Heterogeneous Information ◽  
Industry Sector ◽  
Information Spread ◽  
The Right

Enterprise Resource Planning (ERP) systems have emerged as solutions oriented to manage organizations’ resources in an integrated way. They allow the automation of department activities, make information available to users at the right time, and support more accurately their decision-making needs. However, although the implementation of these systems has brought considerable benefits to users, they do not cover all processes from all industries. Many organizations have recognized this limitation and consequently felt the need to implement specific solutions to their industry, sector, or line of business. From the collected case study business drivers and objectives, the authors analyze the advantages and limitations of ERP Systems in the hospitality industry in order to understand how this industry uses ERP Systems and solves the challenge of integrating information spread through several heterogeneous information systems.

Whistleblowing and Boundary Violations: exposing a colleague in the forensic milieu

2003 ◽  
Vol 10 (5) ◽  
pp. 526-537 ◽  
Author(s):  
Cindy Peternelj-Taylor
Keyword(s):  
Decision Making ◽  
Nursing Practice ◽  
Clinical Situation ◽  
Psychiatric Nursing ◽  
Boundary Violations ◽  
Boundary Violation ◽  
Forensic Psychiatric ◽  
The Right

The purpose of this article is to examine the phenomenon of whistleblowing as it relates to a reconstructed case study of an erotic boundary violation that emerged from a clinical situation in forensic psychiatric nursing practice. The unique features of this case are illustrated with the help of a model for decision making. Although the ramifications of exposing a colleague are many, it is argued that, in this particular case, it was morally and ethically the right thing to do.

Key Techniques in Intelligent Predictive Maintenance (IPdM) – A Framework of Intelligent Faults Diagnosis and Prognosis System (IFDaPS)

2014 ◽  
Vol 1039 ◽  
pp. 490-505 ◽  
Author(s):  
Ke Sheng Wang
Keyword(s):  
Decision Making ◽  
Frequency Domain ◽  
Wavelet Packet ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Ann Model ◽  
Diagnosis And Prognosis ◽  
Faults Diagnosis ◽  
Maintenance Decision

Intelligent predictive maintenance (IPdM) is a maintenance strategy that makes maintenance decisions automatically and dynamically based on Artificial Intelligence and Data mining techniques through condition monitoring of machines, equipment and production processes. IPdM system consists of the following main modules: sensor and data acquisition, signal and data processing, feature extractions, maintenance decision-making, key performance indicators, maintenance scheduling optimization and feedback control and compensation. Among them, the most important part of IPdM is maintenance decision-making, which includes diagnostics and prognostics. This paper proposes a framework of intelligent faults diagnosis and prognosis system (IFDaPS) and discuss some key technologies for implement IPdM policy in manufacturing and industries. A case study focus on the vibration signals collected from the sensors mounted on a pressure blower for critical components monitoring. We decompose the pre-processed signals into several signals using Wavelet Packet Decomposition (WPD), and then the signals are transformed to frequency domain using Fast Fourier Transform (FFT). The features extracted from frequency domain are used to train Artificial Neural Network (ANN). Trained ANN model is able to identify the fault of the components and predict its Remaining Useful Life (RUL). The case study demonstrates how to implement the proposed framework and intelligent technologies for IPdM and the result indicates its higher efficiency and effectiveness comparing to traditional methods.

Choosing action plans for strategic manufacturing objectives using AHP

2019 ◽  
Vol 30 (1) ◽  
pp. 180-194 ◽  
Author(s):  
Andrea Chiarini
Keyword(s):  
Decision Making ◽  
Action Plan ◽  
Decision Making Process ◽  
Action Plans ◽  
Manufacturing Strategy ◽  
Analytic Hierarchy ◽  
Managerial Style ◽  
Content Type ◽  
The Right

Purpose The purpose of this paper is to investigate whether the analytic hierarchy process (AHP) methodology can help in the decision-making process of choosing action plans linked to manufacturing strategy. The research also analyses the path which some managers followed for defining and selecting the action plans as well as the organisational obstacles and pitfalls the managers encountered. Design/methodology/approach The authors conducted an exploratory case study in a medium-sized Italian manufacturing company. The authors collected, coded and discussed data from the AHP implementation. Furthermore, during the observation of how the managers dealt with the decision-making path, the authors collected, coded and discussed the qualitative data. Findings Results showed that AHP made the decision-making process of choosing between alternative plans more objective. However, the authors observed obstacles and pitfalls mainly linked to organisational aspects such as creating team and staff’s awareness, involvement and commitment as well as staff’s skills. Other interesting findings are linked to the creation of managers’ consensus and the top manager’s managerial style and how the latter could affect the AHP consistency ratio. Research limitations/implications This research is based on a case study. The findings need to be tested by other scholars and practitioners in different organisations. Moreover, issues such as management consensus and negotiation in manufacturing organisations and managerial style need further research. Practical implications AHP methodology can help practitioners who are dealing with the deployment of strategic manufacturing objectives and who are trying to employ methods for choosing the right action plan. Besides, practitioners are aware of specific organisational obstacles and pitfalls encountered on the strategic deployment path. Originality/value This paper proposes for the first time the use of the AHP methodology for choosing between action plans derived from strategic manufacturing objectives.

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