Maintenance Applications Research Articles

INCREASING PROFITABILITY OF A MANUFACTURING COMPANY BY USING THE TOTAL PRODUCTIVE MAINTENANCE APPROACH: A REVIEW

In the modern world, a company pays serious attention to productivity and efficiency. Several sectors in the manufacturing world are starting to carry out business transformation, particularly those leading to industry 4.0. This circumstance is driven by efforts to achieve higher productivity and an efficient process, mainly after being economically hit by the Covid-19 pandemic. To maintain efficiency, a manufacturing company should maintain its machine and equipment. Total productive maintenance is a system based on the concept of preventive and predictive maintenance. It is designed to prevent loss resulting from many factors, e.g. production interruption due to failures and adjustments, speed loss due to minor stops and speed reductions, and loss due to defects. Total productive maintenance also provides a tool to measure the level of effectiveness of a machine, i.e., overall equipment effectiveness. This review article focusses on the application of total productive maintenance and overall equipment effectiveness to determine the effectiveness of equipment and eliminate the factors that affect the effectiveness of the equipment.

DOES LEGAL REPRESENTATION INFLUENCE AMOUNT OF AWARDS IN CHILD MAINTENANCE? A QUANTITATIVE ANALYSIS OF SELECTED CASES IN MALAYSIAN SYARIAH COURTS

Background and Purpose: Cases of child maintenance application in courts could have at least one party without counsel representation. Self-representation may lead to litigants’ inability to obtain fair outcomes in their cases. The impact of legal representation and self-representation on case outcomes has not been extensively studied. This study sought to examine whether self-represented mothers or mothers represented by lawyers were the ones who have obtained more promising outcomes in terms of the amounts of award.
 
 Methodology: This quantitative study involved 82 first instance and appeal cases decided in Syariah courts from 1969 to 2020. From these case reports, relevant data on litigants and case decisions were extracted, then statistically analysed using IBM SPSS Statistics version 26. Case reports were retrieved from Current Law Journal, Lexis Nexis, and Jurnal Hukum databases by conducting case searches using a list of search terms including nafkah anak, nafqah, and child maintenance.
 
 Findings: The finding indicated that representation for mothers did not significantly influence the total amounts of awards. Self-represented mothers were also shown to have won higher amounts in terms of their proportion to fathers’ incomes. The amounts of awards were shown to be significantly influenced by evidence relating to fathers’ incomes and child’s expenses. The amounts were higher when these details were presented, as proven by their inclusion in case reports. Self-represented mothers fared better than mothers with lawyers when these determinants were presented.
 
 Contributions: Parties and counsels will be better informed of past trend involving full, semi, and non-representation in cases involving child maintenance orders.
 
 Keywords: Child maintenance, Act 303, legal representation, self-representation, child support.
 
 Cite as: Kadir, R., Mokhlis, S., & Abd Karim, J. (2023). Does legal representation influence amount of awards in child maintenance? A quantitative analysis of selected cases in Malaysian Syariah courts. Journal of Nusantara Studies, 8(1), 270-289. http://dx.doi.org/10.24200/jonus.vol8iss1pp270-289

Towards Deploying DNN Models on Edge for Predictive Maintenance Applications

Almost all rotating machinery in the industry has bearings as their key building block and most of these machines run 24 × 7. This makes bearing health prediction an active research area for predictive maintenance solutions. Many state of the art Deep Neural Network (DNN) models have been proposed to solve this. However, most of these high performance models are computationally expensive and have high memory requirements. This limits their use to very specific industrial applications with powerful hardwares deployed close the the machinery. In order to bring DNN-based solutions to a potential use in the industry, we need to deploy these models on Microcontroller Units (MCUs) which are cost effective and energy efficient. However, this step is typically neglected in literature as it poses new challenges. The primary concern when inferencing the DNN models on MCUs is the on chip memory of the MCU that has to fit the model, the data and additional code to run the system. Almost all the state of the art models fail this litmus test since they feature too many parameters. In this paper, we show the challenges related to the deployment, review possible solutions and evaluate one of them showing how the deployment can be realized and what steps are needed. The focus is on the steps required for the actual deployment rather than finding the optimal solution. This paper is among the first to show the deployment on MCUs for a predictive maintenance use case. We first analyze the gap between State Of The Art benchmark DNN models for bearing defect classification and the memory constraint of two MCU variants. Additionally, we review options to reduce the model size such as pruning and quantization. Afterwards, we evaluate a solution to deploy the DNN models by pruning them in order to fit them into microcontrollers. Our results show that most models under test can be reduced to fit MCU memory for a maximum loss of 3% in average accuracy of the pruned models in comparison to the original models. Based on the results, we also discuss which methods are promising and which combination of model and feature work best for the given classification problem.

Comparison of population-based algorithms for parameter identification for induction machine modeling

PurposeMonitoring and diagnosis of fault cases for squirrel cage induction motors can be implemented using the multiple coupled circuit model. However, the identification of the associated model parameters for a specific machine is problematic. Up to now, the main options are measurement and test procedures or the use of finite element method analyses. However, these approaches are very costly and not suitable for use in an industrial application. The purpose of this paper is a practical parameter identification based on optimization methods and a comparison of different algorithms for this task.Design/methodology/approachPopulation-based metaheuristics are used to determine the parameters for the multiple coupled circuit model. For this purpose, a search space for the required parameters is defined without an elaborate analytical approach. Subsequently, a genetic algorithm, the differential evolution algorithm and particle swarm optimization are tested and compared. The algorithms use the weighted mean squared error (MSE) between the real measured data of stator currents as well as speed and the simulation results of the model as a fitness function.FindingsThe results of the parameter identification show that the applied methodology generally works and all three optimization algorithms fulfill the task. The differential evolution algorithm performs best, with a weighted MSE of 2.62, the lowest error after 1,000 simulations. In addition, this algorithm achieves the lowest overall error of all algorithms after only 740 simulations. The determined parameters do not completely match the parameters of the real machine, but still result in a very good reproduction of the dynamic behavior of the induction motor with squirrel cage.Originality/valueThe value of the presented method lies in the application of condition-based maintenance of electric drives in the industry, which is performed based on the multiple coupled circuit model. With a parameterized model, various healthy as well as faulty states can be calculated and thus, in the future, monitoring and diagnosis of faults of the respective motor can be performed. Essential for this, however, are the parameters adapted to the respective machine. With the described method, an automated parameter identification can be realized without great effort as a basis for an intelligent and condition-oriented maintenance.

3D mapping and photogrammetry sensor payload for unmanned aerial vehicles

Unmanned Aerial Vehicles (UAVs) have shown great potential for data collection and monitoring of areas. Sensors such as LiDARs and cameras can be used on UAVs for high-resolution data collection and used for various applications like Digital Surface Models (DSM), photogrammetry, inspection systems, and maintenance applications. This paper describes the design and implementation of a sensor payload for UAVs using a LiDAR sensor, camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS). The payload collects data that can be used to georeference LiDAR and camera data, which can later be used to generate a georeferenced map and perform object detection and classification.

Development and Tribological Analysis of Bio-Based Lubricant Using Two Non-Edible Oils: Castor Oil and Neem Oil

Lubricants play a very important role in multiple industries in minimizing wear and friction between two moving parts. Most lubricants are produced from petroleum oil which is poorly biodegradable and toxic in nature. Hence these lubricants are highly undesirable due to environmental hazards and the health and safety of the operator. Bio-Lubricants like Castor Oil have found a potential scope in research and are being used in various applications as a substitute for these chemical lubricants. This paper examines the Tribological Properties of developed Bio-Lubricants, prepared using Castor Oil and Neem Oil in a mixing ratio of 20%. Wear and Friction analysis has been carried out using Pin on Disc apparatus at varying loads, which has shown that the blend of Castor Oil as a base with 20% Neem Oil has promising properties, with around 70% reduction in wear rate at specific loads, hence showcasing a tremendous capacity for being used in maintenance applications. An overall reduction in wear and coefficient of friction was observed in the developed mixture, making it a potential environment-friendly replacement for chemical lubricants and an upgrade over Pure Refined Castor Oil.

Combined application of condition-based maintenance and reliability centred maintenance using PFMEA and lean concepts - a case study

Combined application of condition-based maintenance and reliability centred maintenance using PFMEA and lean concepts - a case study

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

Comprehensive Production Maintenance and its Role in Improving the Company Performance

The study aimed to reveal the impact of applying comprehensive production maintenance on company performance in small and medium industrial Malaysian companies. The descriptive analytical secondary data was utilised as a tool for gathering data from 135 companies as a sample from 2009 to 2017, and after conducting the analysis of the data and testing the hypotheses, the study reached a number of results among them. There is a positive significant effect of the application of comprehensive productivity maintenance on company performance in in the small and medium industrial companies. Therefore, this study recommended that the total productive maintenance is a system that is also valuable for quality administration and safety administration in rapports of keeping the integrity of equipment and operating approaches. The study recommended giving more attention to the management of the industrial companies.

An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

Predictive maintenance is a vital aspect of the industrial sector, and the use of Industrial Internet of Things (IIoT) sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions. An integrated approach for acquiring, processing, and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge. This study presents an IIoT-based sensor node for industrial motors. The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms. The initial step of signal processing is performed on the node at the edge, reducing the burden on a centralized cloud for processing data from multiple sensors. The proposed architecture utilizes the lightweight Message Queue Telemetry Transport (MQTT) communication protocol for seamless data transmission from the node to the local and main brokers. The broker’s bridging allows for data backup in case of connection loss. The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation, ensuring its performance and accuracy in real-world industrial environments. The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults. The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through real-time monitoring and early fault detection, ultimately leading to minimized unscheduled downtime and cost savings.

Digital Twins for predictive maintenance: A case study for a flexible IT-architecture

The Digital Twin offers great potential for use in predictive maintenance applications to prevent failures of critical components. However, due to the absence of standardized defined functionalities of Digital Twins and the associated software tools, the creation of Digital Twins is linked to a high expenditure of resources. This effort is due to static connections of components within the IT-architecture, which cannot be reused for other Digital Twins, as these IT-architectures are mainly single-case solutions. Although there are approaches for creating flexible IT-architectures, these have not yet been examined in depth for their reusability to build Digital Twins with different implementation conditions. Therefore, the goal of this case study is to investigate the reusability and the associated reduction of effort involved in building Digital Twins. For this purpose, the approaches of an IoT-platform and a service-oriented architecture are combined to create a reusable and flexible IT-architecture for Digital Twins. This IT-architecture is applied to a first use case of a Digital Twin for a compressor and then adapted to a second use case of a Digital Twin for a wind turbine. These use cases have different implementation conditions, but both find application in predictive maintenance. Finally, the reusability is evaluated and, in this context, the extent to which the resources required for building Digital Twins can be reduced when utilizing a flexible IT-architecture.

Predictive maintenance analytics and implementation for aircraft: Challenges and opportunities

AbstractThe increase in available data from sensors embedded in industrial equipment has led to a recent rise in the use of industrial predictive maintenance. In the aircraft industry, predictive maintenance has become an essential tool for optimizing maintenance schedules, reducing aircraft downtime, and identifying unexpected faults. Despite this, there is currently no comprehensive survey of predictive maintenance applications and techniques solely devoted to the aircraft manufacturing industry. This article is an in‐depth state‐of‐the‐art systematic literature review of the different data types, applications, projects, and opportunities for predictive maintenance in this industry. The goal of this review is to identify, and highlight the challenges and opportunities for future research in this field. This review found that the current focus of research is too biased towards aircraft engines due to a lack of publicly available data sets, and that greater automation is an important step to optimize aircraft maintenance to its full potential.

VR-based computer maintenance practical training platform development design and application research

Abstract The development of computer maintenance has now become the focus of attention in the education sector, standing in the overall situation of economic, social and educational development, to comprehensively improve the level of computer maintenance education, and teaching quality has become an important measure to meet the new round of technological revolution and industrial change. The traditional teaching model has not been able to adapt to the industry and enterprise demand for job competence, with the development of VR technology computer maintenance training platform. This study uses the NTM-VR model as the theoretical basis and incorporates the data of traditional practical training platform and VR practical training platform into the model for analysis and calculation. The calculation results show that the acceptance of the VR practical training platform by college students is quite high at 67.4%, much higher than 32.6% of the traditional platform. And under the uniform learning intensity, the learning outcome based on the VR computer maintenance practical training platform is 76%, much higher than that based on the traditional computer maintenance practical training platform of 24%.

Autonomous Maintenance: A Case Study on Step – 0 to 3 Implementation at Detergent Packing Machine in FMCG industry

Businesses in the Fast-Moving Consumer Goods (FMCG) sector always work to improve the efficiency of their manufacturing processes in order to keep a competitive edge in the fast-paced and cutthroat business. With a focus on the detergent packing machine at an FMCG manufacturing plant, this case study explores the application of autonomous maintenance (AM), a key component of the Total Productive Maintenance (TPM) philosophy. Finding and removing losses from the manufacturing process, especially those involving equipment and plant, is the main goal of AM. The four main phases of the step-by-step process described in the AM technique are Step0, Step1, Step2, and Step3, each of which aims to restore the fundamental machine conditions. The AM process's cornerstone, Step 0, emphasizes the necessity of a change in the maintenance staff's perspective. In this level, operators and maintenance teams would discover the value of autonomous maintenance and be instilled with a sense of ownership and responsibility for the equipment. The emphasis switches to cleaning and inspection tasks in Step 1. The detergent packing machine is thoroughly cleaned and carefully inspected to look for any indications of wear and tear or possible problems. This stage lays the groundwork for future enhancements by attempting to create a baseline for the machine's current state. The second step is to establish performance and cleanliness standards for the machine. For regular cleaning and inspections, standardized protocols are created to provide uniformity and predictability in maintenance tasks. In order to improve operators' and maintenance personnel's abilities to recognize and handle possible problems, this stage also includes training courses for them. Step 3 focuses on maintaining the progress made in the preceding steps. It entails creating an organizational culture of proactive maintenance where the idea of continuous improvement permeates every aspect of the business. To make sure that the set standards are being followed and that any deviations are quickly corrected, audits and reviews are carried out on a regular basis. This case study demonstrates the effective use of autonomous maintenance at the detergent packing machine in the FMCG sector by carefully following these four guidelines. The outcomes include a significant reduction in costly breakdowns, improved machine reliability, and the cultivation of a culture of continuous improvement, contributing to the overall competitiveness of the company's products. The lessons learned from this case study can serve as valuable insights for other companies looking to embark on a similar journey of enhancing their manufacturing processes through Autonomous Maintenance.

Agnostic learning for packing machine stoppage prediction in smart factories

The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as "agnostic". In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases.

Sistem Informasi Anggaran Biaya Pemeliharaan, Kerusakan Dan Konstruksi Pada Bagian Administrasi

Maintenance and Construction of West Bandung Witel is one of the work units of PT. Telkom Akses Jawa Barat, whose job is to build and maintain the broadband network owned by PT. Telkom Indonesia which is located in Dayeuh Kolot, Bandung City. In improving performance and activities within the Construction and Maintenance Unit environment, a system or application is needed that can facilitate work data collection. So that the work carried out by the unit can be recorded properly and on time according to the targets set by the company. Based on these problems, with this practical work a web-based system or application is built that can help with existing problems. Making this information system is done by way of data collection, analysis, design, implementation up to testing the information system. The end result of this research is a Maintenance and Construction Administration Information System that can be used to facilitate staff or employees in processing work data.
 
 Keywords : Administrative Information Systems, web, PHP, Laravel Framework

SDN-enabled vehicular networks: Theory and practice within platooning applications

With the recent developments of communication technologies surrounding vehicles, we witness the simultaneous availability of multiple onboard communication interfaces on vehicles. While most of the current interfaces already include Bluetooth, WiFi, and LTE, they are augmented further by IEEE 802.11p and the 5G interfaces, which will serve for safety, maintenance, and infotainment applications. However, dynamic management of interfaces depending on application becomes a significant issue that can be best addressed by Software Defined Networking (SDN) capabilities. While SDN-based vehicular networks have been promoted previously, none of these works deal with practical challenges. In this paper, we propose and develop a practical framework that realizes SDN-based vehicular networks for a wide range of applications. Through this framework, we demonstrate a truck platooning application as a use case in which the two truck platoons strive to merge and establish connectivity. The route from source vehicle (i.e., Platoon Leader A) to destination (i.e., Platoon Leader B) is computed with the help of the SDN Controller to transmit the Beacon Safety Messages through Road Side Units (RSUs) at the MAC layer without relying on IP for proper platooning operations. The results show the efficiency of the SDN-based approach compared to the traditional routing approaches.

Research on the Derated Power Data Identification Method of a Wind Turbine Based on a Multi-Gaussian-Discrete Joint Probability Model.

This paper focuses on how to identify normal, derated power and abnormal data in operation data, which is key to intelligent operation and maintenance applications such as wind turbine condition diagnosis and performance evaluation. Existing identification methods can distinguish normal data from the original data, but usually remove power curtailment data as outliers. A multi-Gaussian-discrete probability distribution model was used to characterize the joint probability distribution of wind speed and power from wind turbine SCADA data, taking the derated power of the wind turbine as a hidden random variable. The maximum expectation algorithm (EM), an iterative algorithm derived from model parameters estimation, was applied to achieve the maximum likelihood estimation of the proposed probability model. According to the posterior probability of the wind-power scatter points, the normal, derated power and abnormal data in the wind turbine SCADA data were identified. The validity of the proposed method was verified by three wind turbine operational data sets with different distribution characteristics. The results are that the proposed method has a degree of universality with regard to derated power operational data with different distribution characteristics, and in particular, it is able to identify the operating data with clustered distribution effectively.

Near-optimal multi-accelerator architectures for predictive maintenance at the edge

Predictive maintenance systems face a rich set of constraints along dimensions such as latency, throughput, physical size, monetary cost, as well as energy and power consumption.To meet performance requirements, predictive maintenance systems require specialized compute units (i.e., accelerators) in addition to conventional processor cores. Unfortunately, size and cost constraints commonly result in developers being forced into selecting System-on-Chip (SoC) platforms that do not have sufficient resources to fully accelerate all performance-critical functions — in essence raising the challenging question of how to optimally distribute the available resources across accelerators. This work introduces the Resource-Constrained Accelerator Selection (RCS) methodology, which identifies near-optimal multi-accelerator configurations for predictive maintenance applications.RCS takes a library of resource-scalable accelerator architectures as input and then selects the combination of accelerator configurations that minimizes end-to-end latency. We find that enabling RCS for typical predictive maintenance applications requires a resource-scalable Fast Fourier Transform (FFT) accelerator and propose ScaleFFT to fill this gap.We apply RCS and ScaleFFT to a collection of edge computing applications with different sensor bandwidths and find that they reduce end-to-end latency by 2.4× on average for a 256K-point FFT compared to a state-of-the-art configuration that only accelerates the machine learning algorithm. Moreover, we demonstrate that RCS enables real-world gains in oil well and train track monitoring systems.

Analytic hierarchy process methodology in part selection for fused deposition modelling in the South African railway industry

This study proposes a methodology to identify, evaluate and select parts, components and products within the railway environment that will benefit from the Fused Deposition Modelling Additive Manufacturing technology at the initial conceptual design phase. A review on the state-of-the-art in Multi-Criteria Decision-Making models is presented, focusing on the Fused Deposition Modelling Additive Manufacturing process. The criteria identified in the literature were used to develop a custom evaluation model for part selection specific to railway maintenance applications using the Analytic Hierarchy Process. The proposed methodology is validated using case studies from published literature and then applied to railway-specific case studies to determine its benefit using the Fused Deposition Modelling technology. The railway case studies present functional end-use 3D printed replacement parts, custom tooling and prototype products for railway infrastructure and rolling stock vehicles. Lastly, the proposed methodology was integrated into a program using Visual Basic and Microsoft Excel to assist novice railway engineers in identifying whether parts will benefit from the 3D printing process.