Unplanned Maintenance Research Articles

Integrating spare part inventory management and predictive maintenance as a digital supply chain solution

Purpose This study aims to assess the feasibility and effectiveness of incorporating predictive maintenance (PdM) into existing practices of spare part inventory management and pinpoint the barriers and identify economic values for such integration within the supply chain (SC). Design/methodology/approach A two-staged embedded multiple case study with multi-method data collection and a combined discrete/continuous simulation were conducted to diagnose obstacles and recommend a potential solution. Findings Several major organisational, infrastructure and cultural obstacles were revealed, and an optimum scenario for the integration of spare part inventory management with PdM was recommended. Practical implications The proposed solution can significantly decrease the inventory and SC costs as well as machinery downtimes through minimising unplanned maintenance and addressing shortage of spare parts. Originality/value This is the first study with the best of our knowledge that offers further insights for practitioners in the Industry 4.0 (I4.0) era looking into embarking on digital integration of PdM and spare part inventory management as an efficient and resilient SC practice for the automotive sector by providing empirical evidence.

Failure Pattern and Reliability Analysis of Mobile Biomass Gasifier Engine (Prototype 3) using Weibull Calculation as a Basis for Updating the Preventive Maintenance Activity Schedule

Gasification is a technology that optimistically utilizes biomass to produce syngas (consisting of H2, CO, CO2, CH4) that can be used as an energy source while reducing excess CO2 emissions. However, if there are frequent failures due to unplanned maintenance activities, the engine performance will not be optimal. Therefore, an up-to-date reliability study is needed as a basis for updating preventive maintenance activities to maintain the quality of the engine so that it continues to operate properly and is durable. This research aims to analyze the reliability, unreliability, and availability of the Mobile Biomass Gasifier (Prototype3), as well as study the failure patterns measured using the shape parameter (?) with the Weibull distribution. The focus of this research is on the Suction blower which based on Pareto results is the equipment with the highest failure frequency. The results show that the average reliability is smaller than the failure rate. In addition, the value of the shape parameter (?) > 1, which means that the damage rate increases as the component ages. The implication of this study shows the need to update the preventive maintenance (PM) schedule by considering the addition of new PM activities that are in accordance with the duration of the life of the components that cause machine damage.

Combining Artificial Neural Networks and Mathematical Models for Unbalance Estimation in a Rotating System under the Nonlinear Journal Bearing Approach

Rotating systems are essential components and play a critical role in many industrial sectors. Unbalance is a very common and serious fault that can cause machine downtime, unplanned maintenance, and potential damage to vital rotating machines. Accurately estimating unbalance in rotor–bearing systems is crucial for ensuring the reliable and efficient operation of machinery. This research paper presents a novel approach utilizing artificial neural networks (ANNs) to estimate the unbalance masses in a multidisk system based on simulation data from a nonlinear rotor–bearing system. Additionally, this study explores the effect of various operating parameters on oil film stability and vibration response through a combination of bifurcation diagrams, spectrum cascades, Poincare maps, and orbit and FFT plots. This study demonstrates the effectiveness of ANNs for unbalance estimation in a fast and accurate way and discusses the potential of ANNs in smart online condition monitoring systems.

Investigation of operational settings, environmental conditions, and faults on the gas turbine performance

Abstract Gas turbine engines are complex mechanical marvels widely employed in diverse applications such as marine vessels, aircraft, power generation, and pumping facilities. However, their intricate nature renders them susceptible to numerous operational faults, significantly compromising their performance and leading to excessive emissions, consequently incurring stringent penalties from environmental regulatory bodies. Moreover, the deterioration of gas turbine performance is exacerbated by variations in working conditions based on operational settings and environmental conditions. Past studies have focused on certain working conditions that limit effectiveness in real-world applications where operational settings and environmental conditions vary during operations. The influence of these working conditions on the performance of gas turbines also needs to be assessed, as they can lead to different fault patterns resulting in unplanned maintenance, unnecessary maintenance costs, unsafe conditions and stringent penalties. This study uses the gas turbine simulation program to simulate a high-bypass turbofan engine inspired by Pratt & Whitney PW-4056, analysing the combined effects of operational settings and environmental conditions on engine performance while also incorporating simulations of common gas turbine faults like fouling and erosion in various locations and severities along the gas path. The model’s accuracy is confirmed by low mean absolute percentage errors of 0.004% of thrust at the cycle reference point and 0.15% and 0.28% at 2 km and 7 km altitudes, respectively, demonstrating the model’s robustness across varying operational scenarios. In conclusion, this research highlights the significant effects of operational settings and environmental factors on gas turbine performance, particularly impacting specific fuel consumption and thrust. The study reveals that operational settings and environmental factors significantly impact fuel consumption and thrust. Specifically, compressor fouling and low-pressure turbine erosion increase nitrogen oxide (NOx) emissions by 4.5% and 11.1%, while fouling of nozzle guide vanes and high-pressure turbine erosion raise unburned hydrocarbon by 10.0% and 20.2%, and carbon monoxide (CO) by 3.2% and 5.2%, respectively, compared to a healthy engine. These insights highlight the importance of component-specific degradation in influencing gas turbine performance and emissions.

Group machinery intelligent maintenance: Adaptive health prediction and global dynamic maintenance decision-making

Intelligent preventive maintenance powered by health data analytics is essential to ensure operation safety and performance of diverse industrial equipment. Despite the rapid advancement of preventive maintenance methodologies in recent several decades, their implementation to global dynamic maintenance of complex systems across infinite time horizon, in particular leveraging real-time prognosis information to realize adaptive group partition and ordering, has been largely an under-explored domain. To this end, this study devises a generic global-dynamic group maintenance policy for multi-component degrading systems. As opposed to existing models, the policy automatically renews the entire system health information instantly upon the completion of each group maintenance task; therefore, it realizes the dynamic union of (a) predictive group maintenance and (b) unplanned opportunistic maintenance over an infinite time horizon for the first time, such that to promote decision precision and efficiency. A dynamic grouping algorithm is designed to explore the structure of optimal maintenance solutions. The applicability is exemplified by comparative experiments that show substantial advantages over heuristic policies.

Machine Learning Models for Predictive Maintenance in Industrial Engineering

Purpose: The general objective of this study was to investigate machine learning models for predictive maintenance in industrial engineering. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.Findings: The findings reveal that there exists a contextual and methodological gap relating to machine learning models for predictive maintenance in industrial engineering. The research highlighted the transformative potential of machine learning models in optimizing predictive maintenance for industrial engineering, demonstrating significant reductions in unplanned downtime and maintenance costs. It identified the strengths of various machine learning approaches, such as supervised, unsupervised, and reinforcement learning, in predicting equipment failures and optimizing maintenance schedules. Despite the benefits, challenges such as data quality, integration complexity, and the need for specialized skills were noted. Future advancements in machine learning, IoT data, and computational power were expected to further enhance predictive maintenance systems, making them more accurate, efficient, and widely adopted across industries.Unique Contribution to Theory, Practice and Policy: The Systems Theory, Diffusion of Innovations Theory and Resource-Based View (RBV) Theory may be used to anchor future studies on machine learning models for predictive maintenance in industrial engineering. This study provided several recommendations that contributed to theory, practice, and policy. It emphasized the development of hybrid machine learning models, integration of domain-specific knowledge, and real-time data collection using IoT technologies. It suggested standardized data protocols and personnel training for better implementation and efficiency. Policy recommendations included regulatory frameworks, incentives for technology adoption, data sharing, and robust data privacy guidelines. These contributions aimed to enhance the accuracy and applicability of predictive maintenance models, improve industrial maintenance practices, and support technological innovation through supportive policies.

A critical analysis of public private partnership model in energy from waste projects

The Public Private Partnerships (PPP) and the Private Finance Initiative (PFI) have been significantly adopted over the last three decades as a mechanism for the delivery and financing of projects. Various infrastructure project types in the energy and the transport sectors have adopted this procurement system for their ‘anticipated’ effectiveness in risk distribution and value for money. Yet, their acclaimed benefits, particularly in ensuring value for money in the waste to energy – or energy-from-wastes (EfW) – sector are under significant scrutiny. Governments and investors are increasingly questioning their fiscal efficiency and overall benefit to taxpayers. Despite these concerns, studies investigating whether PFI provides value of money of PFI in EfW are lacking. This study addresses this gap by comparing the PFI models used in the UK and Canada in currently operative EfW projects using mixed risk epistemology approach combining methods that provide both a real and objective evaluation of issues and risks and a subjective (or opinion-based) evaluation. The former is achieved through a quantitative probabilistic model for simulating EfW feasibility at the operation phase. The quantitative probabilistic model is capable to model and capture how operational phase costs and profitability are affected by lifecycle risks (such as quality of service driven by contractual requirements that are usually passed on to the Operation and Maintenance (O&M) contractor) and a range of technical, payment and incentive dynamic variables and their fluctuations. If the impact of these variables is not considered and modelled accurately, the O&M contractor could incur significant losses undermining the viability of the EfW project. The latter employs a psychometric methodology to delve into the primary concerns of professionals involved in EfW projects regarding associated risks. The results show that common O&M risks identified in EfW PPP projects in UK and Canada prevail around unplanned maintenance, infeed waste reduction, market price, unsustainable debts, and policy changes.

A novel prognostics solution for accurate identification of degradation patterns in turbo machines with variable observation window

The degradation of a system is a time bound phenomenon, which leads to the deterioration of turbomachinery, in terms of performance and reliability. If undetected and not acted upon in time, this could also lead to sudden system failure, resulting in unplanned unit downtime and maintenance. Unplanned downtime of a turbomachine leads to severe production loss for the end customer and consequent economic damages. Early detection of a degradation pattern would provide the customer with the opportunity to timely carry out corrective actions, preventing an unscheduled down time. The paper evaluates degradation identification methodology currently known from literature and finds them not accurate enough for general purpose application required by the solution. The paper discusses a novel methodology which can accurately detect degradation patterns of timeseries data. Critical features of this methodology are novel time-based correlation enabled regression model with variable observation window, autonomous training, and automatic adjusting capability to incorporate operating behavior change or physical system replacement. This leads to high accuracy, high generalization, and domain agnostic application capability. Moreover, particular focus is given to achieving high probability of detection and a low probability of false alarm. The paper demonstrates the performance achieved by the methodology when applied to the field of prognostics and diagnostics of IoT connected turbomachines through 50+ real application cases.

A Digital Twin Development Framework for an Electrical Submersible Pump (ESP)

Premature failure of a sub-system can be critical for an industrial Cyber-Physical System (CPS). A digital twin (DT) assisted predictive maintenance procedure can reduce the risk of costly unplanned maintenance. This study presents a generalized DT development framework for an Electrical Submersible Pump (ESP) that can assist in predictive maintenance. The framework is applied on a single-phase ESP as a proof of concept. The maximum winding temperature of the selected ESP is simulated using a multiphysics simulation tool with transient electromagnetic and transient heat transfer solvers. The simulation parameters were refined using data captured through an ESP free-run experiment. Simulating the total energy loss in the ESP stator and rotor and the transfer of heat from the outer fluid domain facilitates a relationship between the measurable external temperature and the maximum temperature in the stator winding. Following a design of experiments (DOE) approach, a series of simulations were run to establish a statistical model for the winding temperature in terms of the fluid temperature, the time duration a particular temperature was persistent, and the initial maximum stator winding temperature. As the instantaneous maximum stator winding temperature is related to the remaining useful lifetime, it was shown using a case study that the proposed framework can prognosticate the ESP failure, assisting effective decision-making for predictive maintenance of a CPS.

INTELLIGENT MONITORING SYSTEMS IN MANUFACTURING: CURRENT STATE AND FUTURE PERSPECTIVES

Intelligent Monitoring Systems (IMS) have emerged as indispensable tools in modern manufacturing, offering real-time insights into production processes, equipment performance, and quality control. This review provides an overview of the current state and future prospects of IMS in manufacturing environments. The current state of IMS in manufacturing involves the integration of advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics. These systems collect vast amounts of data from sensors, machines, and production lines, enabling real-time monitoring and analysis of various parameters. By employing AI and ML algorithms, IMS can identify patterns, predict anomalies, and optimize production processes, leading to increased efficiency, reduced downtime, and improved product quality. IMS play a crucial role in predictive maintenance, where they can anticipate equipment failures before they occur, thereby minimizing unplanned downtime and maintenance costs. Moreover, IMS facilitate condition-based monitoring, allowing manufacturers to monitor the health and performance of machinery in real-time and schedule maintenance activities accordingly, optimizing resource allocation and prolonging equipment lifespan. Furthermore, IMS contribute to quality control by continuously monitoring production processes and detecting deviations from desired specifications. By leveraging AI-driven algorithms, IMS can automatically adjust process parameters to maintain product quality standards and minimize defects, thereby enhancing overall product reliability and customer satisfaction. Looking ahead, the future perspectives of IMS in manufacturing are promising, with advancements in areas such as edge computing, robotics, and augmented reality poised to revolutionize manufacturing operations further. Edge computing enables data processing and analysis to occur closer to the data source, reducing latency and enhancing real-time decision-making capabilities. Robotics integration with IMS facilitates autonomous manufacturing processes, while augmented reality technologies provide intuitive interfaces for operators to interact with IMS data in real-time. IMS represent a transformative technology in manufacturing, offering unprecedented levels of visibility, control, and optimization. As technology continues to evolve, IMS are poised to play an increasingly vital role in shaping the future of manufacturing, driving efficiency, productivity, and innovation..
 Keywords: Monitoring, System, Intelligent, Manufacturing, Review, Perspectives.

An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence

AbstractWhile wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that operation and maintenance costs are dominant in their cost structure due to unplanned maintenance resulting in energy production loss. Accurate and high performance simulation forecasting tools are required to improve the efficiency and safety of wave converters. This paper proposes a new methodology for significant wave height forecasting. It is based on incorporating swarm decomposition (SWD) and multi‐strategy random weighted grey wolf optimizer (MsRwGWO) into a multi‐layer perceptron (MLP) forecasting model. This approach takes advantage of the SWD approach to generate more stable, stationary, and regular patterns of the original signal, while the MsRwGWO optimizes the MLP model parameters efficiently. As such, forecasting accuracy has improved. Real wave datasets from three buoys in the North Atlantic Sea are used to test and validate the forecasting performance of the proposed model. Furthermore, the performance is evaluated through a comparison analysis against deep‐learning based state‐of‐the‐art forecasting models. The results show that the proposed approach significantly enhances the model's accuracy.

Analysis of the Influence of Gas Leakage in The Dipleg of Cyclone Separators on Particle Properties

The FCC unit uses a cyclone separator for gas-solid separation of catalysts from flue gas or oil and gas. When the cyclone separator experiences a gas leakage fault, it directly affects its separation performance, causing excessive catalyst emissions and leading to the occurrence of catalyst running faults. However, there is currently a lack of diagnostic technology for cyclone separator equipment malfunction and gas leakage. Therefore, the influence of the dipleg of the cyclone separator on the particle loss rate under different gas leakage velocities was investigated experimentally. Experiments show that under normal conditions, particles are subject to multiple cycle separation, and fewer particles randomly enter the riser with the upward rotating flow to escape, which is mainly determined by the structure size and inlet velocity of the cyclone separator. When gas leakage occurs in the dipleg, the higher the gas leakage velocity, the higher the particle loss rate. As the number of particle cycles increases, the loss rate gradually decreases. For this purpose, the cyclone separator inside the FCC unit in the industrial site is taken as the object. In response to multiple cyclone separator loss faults, the changes in process parameters and collected catalyst particle properties of the cyclone separator are analyzed, and the changes in the running dose inside the device during operation are analyzed. Based on the analysis results, the operation of the unit is optimized and adjusted to avoid unplanned shutdown and maintenance. Therefore, based on this, a fault diagnosis technology for catalyst loss in FCC unit cyclone separators based on particle property parameters has been created, which has the characteristics of accuracy and reliability and can be applied to the analysis and diagnosis of catalyst loss faults in FCC unit.

Real-Time Predictive Analytics in Multi-Phase Flow Metering for Offshore Pipelines: A Machine Learning Approach

This research investigates the deployment of real-time predictive analytics in multi-phase flow metering systems for offshore pipelines, with a focus on Nigeria’s oil and gas industry. Offshore operations in Nigeria, a major global oil producer, are often confronted with challenging environmental conditions and the complexities of multi-phase flows—simultaneous flows of oil, water, and gas within pipelines. These factors complicate the accuracy and efficiency of traditional flow measurement systems, leading to potential operational inefficiencies, heightened environmental risks, and costly regulatory non-compliance. Accurate flow measurement is essential in managing resource allocation, preventing over-extraction, and ensuring safe, efficient operations. However, traditional metering methods often fall short in real-time responsiveness and are susceptible to inaccuracies due to variations in pressure, temperature, and flow composition.To address these limitations, this study explores the integration of machine learning with multi-phase flow metering technology, leveraging predictive analytics to enhance real-time monitoring capabilities. Machine learning models, trained on historical and real-time data from flow meters, analyze patterns within the multi-phase flows and forecast pipeline conditions. By identifying potential anomalies, such as leaks, blockages, or unexpected shifts in flow composition, machine learning-based predictive analytics provides operators with timely, actionable insights. These insights enable data-driven decision-making, allowing operators to proactively mitigate risks, optimize production, and align with environmental compliance requirements. Additionally, predictive analytics supports operational efficiency by minimizing the frequency of unplanned maintenance, reducing emissions, and promoting resource optimization.The case study on Resoluto Nigeria Limited—a Nigerian oil and gas service provider—demonstrates the practical application of this technology within a complex offshore environment. Resoluto’s implementation of machine learning-based predictive analytics in multi-phase flow metering exemplifies how data-driven technologies can overcome traditional limitations, offering improved accuracy, enhanced operational resilience, and environmental benefits. The study highlights the advantages of this integration, including reduced operational disruptions, enhanced safety, and improved compliance with environmental regulations. It also examines the challenges specific to the Nigerian context, such as infrastructure constraints, regulatory considerations, and the cost of technology adoption.The findings from this research suggest that machine learning-powered predictive analytics has significant potential for broader application within Nigeria’s offshore oil sector. By reducing environmental risks, improving data reliability, and supporting efficient resource management, this approach aligns with Nigeria’s sustainability objectives and strengthens the resilience of the oil and gas industry. Furthermore, the study underscores the importance of continuous research and development to adapt machine learning solutions for varying offshore conditions and operational needs. The results provide a foundation for future research on the scalability of predictive analytics across diverse sectors in Nigeria’s economy, positioning machine learning as a transformative tool in achieving both operational and environmental goals in industrial applications.

On the effectiveness of standards application to threshold setting in vibration condition monitoring in industrial machinery

One of the goals of Industry 4.0 is to minimize unplanned maintenance emergencies and to leverage the efficiency of condition-based, just-in-time labor and repairs. Achieving the benefits of predictive maintenance via an Industry 4.0-style plant requires not only a data capture ecosystem, but also reference values to inform the monitoring system. Effectively estimating the values of monitoring thresholds is not an easy task, especially in those cases where the manufacturer does not provide data, or the machine is not new but the monitoring system has just been installed, therefore a measurement history is not yet available. The purpose of the present work is assess, through the analysis of two separate test cases, whether the monitoring thresholds suggested by different internationally recognized standards may serve as effective starting values in a vibration-based condition monitoring system. While general agreement on the order of magnitude of the thresholds is present in all cases, the differences between the standards allow for conclusions to be drawn on the most convenient format and processing technique to handle the data and to assess the most/least conservative standards. This paper constitutes, to the authors knowledge, the only systematic study in which the recommendations of all available international standards for vibration monitoring of non-rotating parts are applied to the same test cases and compared.

Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model

The operation and maintenance (O&M) issues of offshore wind turbines (WTs) are more challenging because of the harsh operational environment and hard accessibility. As sudden component failures within WTs bring about durable downtimes and significant revenue losses, condition monitoring and predictive fault diagnostic approaches must be developed to detect faults before they occur, thus preventing durable downtimes and costly unplanned maintenance. Based primarily on supervisory control and data acquisition (SCADA) data, thirty-three weighty features from operational data are extracted, and eight specific faults are categorised for fault predictions from status information. By providing a model-agnostic vector representation for time, Time2Vec (T2V), into Long Short-Term Memory (LSTM), this paper develops a novel deep-learning neural network model, T2V-LSTM, conducting multi-level fault predictions. The classification steps allow fault diagnosis from 10 to 210 min prior to faults. The results show that T2V-LSTM can successfully predict over 84.97% of faults and outperform LSTM and other counterparts in both overall and individual fault predictions due to its topmost recall scores in most multistep-ahead cases performed. Thus, the proposed T2V-LSTM can correctly diagnose more faults and upgrade the predictive performances based on vanilla LSTM in terms of accuracy, recall scores, and F-scores.

Experimental investigation into the state estimation of a forestry crane using the unscented Kalman filter and a multiphysics model

To increase productivity, reduce energy use, and minimize unplanned maintenance, manufacturers of heavy machinery must instrument their products. As explained in the literature, state and parameter estimators can successfully integrate machine sensor signals with simulation results from computational models. This leads to comparable or improved observations even when fewer sensors are being used. This study introduces a state observer based on the unscented Kalman filter for the coupled mechanical and hydraulic systems. The resulting reality-driven simulation procedure is applied to a hydraulically actuated forestry crane that has been instrumented to provide the necessary sensor information. This study analyzes the performance of state observer in four different scenarios and recommends an optimal sensor configuration for the application. Estimation accuracy of observer in the simulation of the mechanics and hydraulics components is evaluated using the percent normalized root mean square error (PN-RMSE) and 95% confidence interval.

SLIP HANDLING ON CONVEYORS TO EXTEND THE LIFE OF CONVEYORS AT THE DEPARTURES TERMINAL

The departure terminal’s Abdulrachman Saleh Malang Airport has facilities as an improvement in services for airplane passengers, such as aviobridges, escalators, travelators, elevators, and conveyors. Conveyors are mechanical systems that transfer luggage considerably from one location to another. To facilitate routing procedures, an effective model for conveyor operations must be developed, as well as the maintenance operation. This study aims to determine the consequences of lack of maintenance on the conveyor belt, namely the occurrence of slack in the belt so that it continues to experience abrasions on the sides of the conveyor and tears on the edges of the belt. The tear causes the fibers to unravel so that slip occurs between the belt and the upper idler shaft (carry idler) so that the idler cannot rotate. This causes the conveyor belt to run and malfunction. This research uses a qualitative method of conducting an exploratory study of the conveyor belt using experimental description research by describing and analyzing the existing conditions on the conveyor. The result is that there are belt fibers that cause slippage on the conveyor. Handling that can be done is cutting the fiber belt. The conclusion is that many factors can cause slippage on the conveyor belt, such as too heavy a load, old belt age, and the distance between the collecting belt conveyor and the transfer line conveyor. Maintenance can be done on the equipment to avoid slippage on the conveyor. Conveyor maintenance activities consist of two types, namely unplanned maintenance and planned maintenance. Unplanned maintenance is also called reactive maintenance or emergency maintenance.

103 ‘Remote Workplaces’ – How Wirelessly Connected Wearable Monitoring Devices Provide Insight and Context to the Occupational Hygienist on Potential Workplace Exposures

Abstract Over the past decade wearable devices have been popular amongst the wider population, particularly within the domestic fitness market. Manufacturers of occupational hygiene monitoring equipment have recently released wirelessly enabled wearable instruments designed for the industrial workforce that can provide data in real-time from local or remote workplaces. These instruments include portable gas detectors, heat stress monitors, noise dosimeters and hand arm vibration monitors. Occupational hygienists may be responsible for a company that has multiple sites often widely scattered, nationally and internationally. In their role they may be required to fly offshore to visit platforms in the oil and gas sector or drive long distances to reach isolated mine sites. The costs and additional travel risks can be considerable. Additionally, any interruptions in monitoring personal exposures on site may render the trip unsuccessful. Wearable instruments allow the occupational hygienist to detect change in a remote workplace in real-time, alerting the wearer and hygienist to an issue that needs to be addressed, enabling mitigation to take place in a timely manner. Furthermore, receiving instant access to sensor data allows determination of the correct response to unique, uncommon or unplanned maintenance activities that would be difficult to capture. This paper will review some of these monitoring devices, associated software and data outputs. Other technology currently available to the occupational hygienists to monitor for workplace hazards at remote worksites will also be addressed including: Their suitability for exposure assessments, use to evaluate controls, their limitations, and their long-term viability as a tool in the hygienist’s toolbox.

Planned maintenance vs Unplanned maintenance and facility costs

Planned maintenance, preventive, predictive, or reliability-based, are assumed to minimize costs and ensure facility sustainment and provide for high customer satisfaction. Good facility management supports sustainability and occupant satisfaction. Planned maintenance is the most efficient means to serve a facility while unplanned maintenance, reactive response to service failures and interruptions, is costly. Customer survey results can be used to measure the effectiveness of preventive maintenance and identify alternative approaches. This research looks at maintenance records from several North American universities, focusing on planned and unplanned maintenance activities and occupant surveys. The ultimate goal is to find the appropriate mix of planning and unplanned maintenance and achieve occupant satisfaction.

On Sensor Data Clustering for Machine Status Monitoring and Its Application to Predictive Maintenance

Predictive maintenance is one of the main approaches which Industry 4.0 is based on, since it aims at reducing unplanned downtime and maintenance costs of industrial machines. In this work, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-aware clustering-based approach to the analysis of sensor data</i> is presented for the purpose of monitoring the time evolution of the health status of an industrial machine. A possible application of the proposed framework to predictive maintenance is then proposed. As a relevant representative application scenario, the focus is on one of the key machines in a pharmaceutical plant: a freeze-dryer. The illustrated procedure allows to carry out a time segmentation of the properly sensed data. More precisely, the corresponding operational points (associated with features of the sensed data) are clustered using various algorithms, among which Density-Based Spatial Clustering of Applications with Noise (DBSCAN) turns out to be the best. The benefits of the proposed approach are (i) its general nature and (ii) the limited amount of needed features that have to be extracted from a single sensor signal. The proposed procedure is attractive when the collected data (e.g., from a single sensor) are not sufficient to build an accurate physical model of the monitored component.