Asset Monitoring Research Articles

Violence and Illegal Deforestation: The Crimes of “Environmental Militias” in the Amazon Forest

ABSTRACT This article presents a conceptual definition of the “environmental militias,” which are criminal networks responsible for the rampant increase of deforestation in the Brazilian Amazon. We demonstrate how they combine violence and technology to reinforce the economic interests of agribusiness, illegal mining and logging sectors, providing all the logistic chain that interconnects such illegal activities and the global market. We argue that since the election of Jair Bolsonaro “environmental militias” have not only been supported by the president’s discourse, but also by federal institutions and government programs negatively impacting the Brazilian environmental legislation, reducing monitoring, control and surveillance of environmental assets and the power of institutions responsible for demarcating Indigenous Lands. This article demonstrates the struggle of the Brazilian federal government against scientific data related to deforestation and the increase in human caused fires in the Amazon region. Environmental concern based on scientific criteria has been redefined as “leftist ideology” and the government replaced specialized technicians by military members with no expertise in environmental monitoring or oversight institutions. Finally, we critically analyze market discourses on sustainability by understanding them as corporative reputational strategies rather than effective commitment with the preservation of the Amazon Forest.

Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis.

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets. Amongst others, the Persistent Scatterers Interferometry (PSI) technique has proven to be viable for the long-term evaluation of ground scatterers. However, its effectiveness as a routine tool for certain critical application areas, such as the assessment of millimetre-scale differential displacements in airport runways, is still debated. This research aims to demonstrate the viability of using medium-resolution Copernicus ESA Sentinel-1A (C-Band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, “Runway n.3” of the “Leonardo Da Vinci International Airport” in Fiumicino, Rome, Italy was investigated as an explanatory case study, in view of historical geotechnical settlements affecting the runway area. In this context, a geostatistical study is developed for the exploratory spatial data analysis and the interpolation of the Sentinel-1A SAR data. The geostatistical analysis provided ample information on the spatial continuity of the Sentinel 1 data in comparison with the high-resolution COSMO-SkyMed data and the ground-based topographic levelling data. Furthermore, a comparison between the PSI outcomes from the Sentinel-1A SAR data—interpolated through Ordinary Kriging—and the ground-truth topographic levelling data demonstrated the high accuracy of the Sentinel 1 data. This is proven by the high values of the correlation coefficient (r = 0.94), the multiple R-squared coefficient (R2 = 0.88) and the Slope value (0.96). The results of this study clearly support the effectiveness of using Sentinel-1A SAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways, paving the way for the development of more efficient and sustainable maintenance strategies for inclusion in next generation Airport Pavement Management Systems (APMSs).

Recognition of LHD Position and Maneuvers in Underground Mining Excavations—Identification and Parametrization of Turns

The Inertial Measurement Unit (IMU) is widely used in the monitoring of mining assets. A good example is the Polish underground copper ore mines of KGHM, where research work with the use of the IMU has been carried out for several years. The potential of inertial sensors was ensured by the development of advanced analytics using machine learning methods to support the maintenance management of an extensive machine park and machine manufacturer in adapting various construction elements to mining conditions. The key algorithms developed in the field of inertial data concern: identification of cycles and components of the haulage process operations, identification of dynamic overloads, technical diagnostics of rotating elements, assessment of road conditions (bumps, slopes, damages), assessment of the technical condition of the pavement, assessment of the operator’s driving style, and finally the machine location in the mining excavation. One of the key operational contexts, necessary in the development of analytics for underground mining vehicles, is the identification of the turning moment of the machine at the intersection together with the determination of the driving direction and the turn angle. In the case of a mine with a room-and-pillar system, where the excavation system has the Manhattan structure, it is possible to use many simplifications to correctly estimate the machine motion path. The identification of the spatial context and the turning maneuver is of key importance both in the development of the machine location system, but also in multi-dimensional analyzes, including the analysis of dynamic overloads or the assessment of the operator’s driving style and work safety. The article presents a comparison of several mathematical models used for the machine turn detection problem, which were trained and tested on the real-life industrial data recorded using IMU during a single working shift of the self-propelled machine.

Optimal Service Points (OSP) for PHM Enabled Condition Based Maintenance for Oil and Gas Applications

In recent times, the oil and Gas industry has faced many challenges resulting from a tightening climate policy environment on oil and gas exploration, as well as the increasing risk of oversupply due to new discoveries globally. This has given stakeholders in the industry an incentive to integrate new technologies to optimize the operational efficiency of their assets, leading to the optimal recovery of hydrocarbons especially in marginal fields. Various Original Equipment Manufacturers (OEM) now provide different service offerings using data driven methods to provide condition monitoring of assets for oil and gas operators. However, a significant part of the value proposition by OEMs in their service delivery focuses on value generated at the component level with a reduction in asset downtime. This limits the broad economic benefits that a condition-based approach can provide, at the enterprise level. Therefore, the purpose of this paper is to develop a cost benefit analysis framework for assessing the implementation of condition and performance monitoring of oil and gas assets used in surface applications. The framework utilizes a combined technical-economic approach to determine a minimum predictive requirement for the implementation of condition-based principles to maintenance of assets in a hydrocarbon project from first oil to abandonment. This financial analysis framework uses a condition monitoring approach based on prognostics as well as a regression approach for fault detection and system performance. The paper will present a case study to evaluate the costs and benefits associated with implementing a condition-based maintenance approach for a set of valves in a Christmas tree subsystem, as part of a typical onshore production system. The framework illustrated using the case study compares a constant failure rate Time-Based approach to the PHM enabled condition-based maintenance. The results demonstrate that a prognostic enabled system can provide commercial benefits at the component level for a Condition Based Maintenance strategy but not necessarily at the enterprise level for an oil and gas project. The cumulative reduction in downtime at the component level over a project lifecycle offsetting the present value of the total cost of integrating a PHM enabled system into the overall maintenance strategy creates the ideal situation for commercial viability. However, the commercial viability of the PHM integration would depend on the accuracy of predicting failure events and monitoring asset degradation by the PHM enabled system which ultimately defines the performance of the condition-based maintenance approach. The accuracy level of asset failure therefore provides OEMs with a benchmark for executing their condition-based maintenance services with a minimum performance threshold. Secondly, an enterprise level financial viability, as well as OEM profitability in the implementation of a condition-based maintenance approach, requires an Optimal Service Point (OSP) which is a function of the minimum predictive requirement of the PHM system. The utility that the OSP provides is that, it gives the minimum value of the framework’s decision criteria that an operator can use a basis for incorporating a condition-based approach in its maintenance strategy. It also provides the maximum Annual Service Fee (ASF) derived from the cumulative OEM NPV needed for structuring and pricing servitization agreements with operators. This OSP cost benefit analysis approach ultimately provides OEMs and operators with a practical guide in the provision as well as the adoption of condition-based maintenance strategies respectively. It balances the risk of PHM integration by operators with a minimum PHM system performance threshold required for commercial viability for project lifecycle.

Renewable energy management system for Saudi Arabia: Methodology and preliminary results

A Renewable Energy Management System (REMS) has been developed for Saudi Arabia to provide the system operator with the daily day-ahead electrical power generation forecasts. The core models included in REMS are: (i) adapted weather research and forecasting (WRF-solar) model that predicts weather conditions and solar irradiance components based on the aerosol (natural and anthropogenic) properties from CHIMERE chemistry transport model, (ii) renewable power prediction schemes that predict electrical power for specific solar technologies (photovoltaic and concentrating solar power plants) at different scales (plant, substation, area, and country land), and (iii) cutting-edge web-interface and geospatial technologies that enable interactive data visualisation on screens of the power plant characteristics and predicted variables over a four-day time frame (two days prior, current day, and a day ahead). The performance of the REMS was evaluated using the actual power output data from Masdar City 10 MW solar photovoltaic power plant and Shams 100 MW concentrated solar power plant in Abu Dhabi, the United Arab Emirates (UAE). The obtained results show the applicability and accuracy of the modelling framework. Currently, REMS simulates the performance of any set of renewable energy scenarios considering daily day-ahead forecasts. But the eventual use of the system in daily operations coupled with decision-making procedures will ensure efficient monitoring and management of renewable assets over time.

Strategic Study on The Business Development of Regional Owned Enterprises in Tanjungpinang City

PT. Tanjungpinang Makmur Bersama (PT. TMB) is a Regional Owned Company in Tanjungpinang City; the company's current activities are: Running a business in the field of regional asset monitoring services in the form of leasing: stores and places of business by renting out to tenants as small entrepreneurs and traders of essential goods for the people of Tanjung City. This study aims to discover and explore the possibility of how BUMD can develop economic resources. This research focuses on identifying internal and external factors for what happened to PT. Tanjungpinang Makmur Bersama to be able to analyze the right business strategy to be implemented. The data collected in this study came from two sources: The primary data collection method was carried out by direct interviews with informants. The informants in this study are the management of PT. Tanjungpinang Makmur Bersama. Secondary data used in this research can be in the form of documents in the format of Financial Statements, Work Plans and Company Budgets, Company Business Plans. The results of the research are presented qualitatively and quantitatively in this study, while the managerial implications and suggestions are also explained in detail at the end of this study.

Digital Twins for the built environment: learning from conceptual and process models in manufacturing

The overall aim of this paper is to contribute to a better understanding of the Digital Twin (DT) paradigm in the built environment by drawing inspiration from existing DT research in manufacturing. The DT is a Product Life Management information construct that has migrated to the built environment while research on the subject has grown intensely in recent years. Common to early research phases, DT research in the built environment has developed organically, setting the basis for mature definitions and robust research frameworks. As DT research in manufacturing is the most developed, this paper seeks to advance the understanding of DTs in the built environment by analysing how the DT systems reported in manufacturing literature are structured and how they function. Firstly, this paper presents a thorough review and a comparison of DT, cyber-physical systems (CPS), and building information modelling (BIM). Then, the results of the review and categorisation of DT structural and functional descriptions are presented. Fifty-four academic publications and industry reports were reviewed, and their structural and functional descriptions were analysed in detail. Three types of structural models (i.e. conceptual models, system architectures, and data models) and three types of functional models (process and communication models) were identified. DT maturity models were reviewed as well. From the reviewed descriptions, four categories of DT conceptual models (prototypical, model-based, interface-oriented, and service-based) and six categories of DT process models (DT creation, DT synchronisation, asset monitoring, prognosis and simulation, optimal operations, and optimised design) were defined and its applicability to the AECO assessed. While model-based and service-based models are the most applicable to the built environment, amendments are still required. Prognosis and simulation process models are the most widely applicable for AECO use-cases. The main contribution to knowledge of this study is that it compiles the DT’s structural and functional descriptions used in manufacturing and it provides the basis to develop DT conceptual and process models specific to requirements of the built environment sectors.

Waveguide metamaterial rod as mechanical acoustic filter for enhancing nonlinear ultrasonic detection

Nonlinear ultrasonic guided waves are among the most promising new tools for early stage damage detection owing to their high sensitivity and long-range propagation features. However, signatures from instrumentation, transducers, and couplant effects create false positives mixing with the material- or defect-induced nonlinearities, leading to inaccurate measurements. Here, we propose a novel technique using a waveguide metamaterial rod, which acts as a mechanical acoustic filter for suppression of higher harmonic components in the measured signal. The proposed waveguide metamaterial consists of an array of flat axisymmetric ridges arranged periodically on the surface of the rod. It is experimentally demonstrated that the higher harmonic components are filtered when the proposed metamaterial rod is placed at the transmission side, thus removing unwanted nonlinearities from the received signal in a pitch-catch configuration. Furthermore, the application of this method is demonstrated by detecting a discontinuity in the workpiece through its nonlinear response enhanced using the metamaterial. This technique is attractive for early stage material diagnosis in engineering, biomedicine, and health monitoring of critical engineering assets.

Semantic Web and Knowledge Graphs for Industry 4.0

In recent years, due to technological advancements, the concept of Industry 4.0 (I4.0) is gaining popularity, while presenting several technical challenges being tackled by both the industrial and academic research communities. Semantic Web including Knowledge Graphs is a promising technology that can play a significant role in realizing I4.0 implementations. This paper surveys the use of the Semantic Web and Knowledge Graphs for I4.0 from different perspectives such as managing information related to equipment maintenance, resource optimization, and the provision of on-time and on-demand production and services. Moreover, to solve the challenges of limited depth and expressiveness in the current ontologies, we have proposed an enhanced reference generalized ontological model (RGOM) based on Reference Architecture Model for I4.0 (RAMI 4.0). RGOM can facilitate a range of I4.0 concepts including improved asset monitoring, production enhancement, reconfiguration of resources, process optimizations, product orders and deliveries, and the life cycle of products. Our proposed RGOM can be used to generate a knowledge graph capable of providing answers in response to any real-time query.

LTDA-MAC v2.0: Topology-Aware Unsynchronized Scheduling in Linear Multi-Hop UWA Networks

This paper investigates the use of underwater acoustic sensor networks (UASNs) for subsea asset monitoring. In particular, we focus on the use cases involving the deployment of networks with line topologies, e.g., for monitoring oil and gas pipelines. The Linear Transmit Delay Allocation MAC (LTDA-MAC) protocol facilitates efficient packet scheduling in linear UASNs without clock synchronization at the sensor nodes. It is based on the real-time optimization of a packet schedule for a given network deployment. In this paper, we present a novel greedy algorithm for real-time optimization of LTDA-MAC schedules. It produces collision-free schedules with significantly shorter frame duration, and is 2–3 orders of magnitude more computationally efficient than our previously proposed solution. Simulations of a subsea pipeline monitoring scenario show that, despite no clock synchronization, LTDA-MAC equipped with the proposed schedule optimization algorithm significantly outperforms Spatial TDMA.

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual DGA data with 2912 entries was used to compute the performance of KosaNet against other algorithms with multiclass classification ability namely the decision tree, k-NN, Random Forest, Naïve Bayes, and Gradient Boost. Investigative results show that KosaNet demonstrated an improved DGA classification ability particularly when classifying multinomial data.

Effective Hybrid Technique in Security Based Wireless Sensor Network

The Wireless Sensor Network (WSN) is a service provider for remote sensing, including wireless transmission and infrastructure, or for the central monitoring of network assets. Over the years, many researchers have tested the WSN for many tests, such as area Monitoring, Environmental Monitoring, Disaster Management, and Security Monitoring. This does not apply to suggestions for detection and monitoring frameworks. In the present investigation, a strategy is proposed secure directing protocol which offering a versatility. Choice of security, bunch training and high-level cassette results also compared with the LEACH Protocol Control Protocol as progressive network stability.

A simplified methodology for risk analysis of historic centers: the world heritage site of San Gimignano, Italy

PurposeWorldwide, natural hazards are affecting urban cultural heritage and World Heritage Sites, exacerbating other environmental and human-induced threats deriving from deterioration, uncontrolled urbanization and unsustainable tourism. This paper aims to develop a disaster risk analysis in Italian historic centers because they are complex large-scale systems that are cultural and economic resources for the country, as well as fragile areas.Design/methodology/approachA heritage-oriented qualitative methodology for risk assessment is proposed based upon the formalization of risk as a function of hazard, vulnerability and exposure, taking into account the values of cultural heritage assets.FindingsThis work provides a contribution to the body of knowledge in the Italian context of disaster risk mitigation on World Heritage Sites, opening for further research on the monitoring and maintenance of the tangible heritage assets. The application to the site of San Gimignano proves the effectiveness of the methodology for proposing preventive measures and actions that ensure the preservation of cultural values and a safer built environment.Originality/valueThe application of a value-based simplified approach to risk analysis is a novelty for historic centers that are listed as World Heritage Sites.

PENGARUH MANAJEMEN ASET TERHADAP OPTIMALISASI PEMANFAATAN ASET TETAP DI KABUPATEN KARANGASEM

The purpose of this research is to analyze the effect of asset management on optimizing the utilization of fixed assets in Karangasem Regency. This research is an associative research, namely the causality relationship between asset inventory, asset valuation, asset control and control to the level of optimization of fixed assets (land and buildings) owned by Karangasem Regency government. Data collection methods used in this study are through questionnaires / questionnaires. The data analysis technique in this study was quantitative statistical analysis using multiple linear regression models that were completed with the SPSS (Statistical Package for the Social Science) for windows The results of hypothesis testing conducted using multiple linear regression models indicate that: 1). Asset inventory has a positive and significant effect on optimizing the management of fixed assets. 2). Asset valuation has a positive and significant effect on optimizing asset management and 3). Asset control and supervision has a positive and significant effect on optimizing the management of the Karangasem Regency Government assets which shows that any improvement in asset control and supervision will be followed by increased optimization of asset management still the Karangasem Regency government. The implications of this study are a) Asset Inventory has a positive and significant effect on Asset Optimization. This proves that with the implementation of the inventory includes data collection, codification / labeling, grouping and bookkeeping / good administration, it will provide an optimal utilization of assets. b) Asset Valuation has a positive and significant effect on Asset Optimization. Asset valuation conducted by an independent institution is needed by the local government of Karangasem Regency, where the results of this value will be used to determine the value of wealth and information for the application of prices for the assets to be sold. c) Asset Monitoring and Control has a positive and significant effect on Asset Optimization. Supervision and Control have been carried out well by the Karangasem Regency government through the development of the Asset Management Information System.

Management of Movable Assets and the Application of Supply Chain Management Policies in the Department Of Human Settlements, Kwazulu-Natal Provincial Government

The Department of Human Settlements (DoHS) has over the years received reports on lack of proper implementation of the Supply Chain Management, in particular the management of movable assets processes. Asset Management is a sub-component within Supply Chain Management in the DoHS. Asset Management is responsible for the management of all movable assets procured by the department. Movable assets such as departmental vehicles, office furniture, computer sets, are used by the officials in performing their day-to-day functions. Officials in the Department are responsible for the management and safeguarding of the assets assigned to them. The research covered in this article aimed to investigate how the department manages its assets and complies with the transversal policies of Supply Chain Management (SCM), which deal with demand management, acquisition management, utilisation management and disposal management processes. The information covered within this research is from varying records, official documents and departmental policies, dealing directly with the problem at hand, especially the management and monitoring of movable assets. Varying methods of collecting and analysing data were used to achieve valid and reliable results. The researcher analysed and compared various information records that informed findings with regard to risk management and control measures, value for money, responsibility and accountability and intervention strategies that may bring about efficiency and effectiveness in the department with regard to management of movable assets. This was done to address the issue of audit queries received by the Department of Human Settlements.

Robust Silent Localization of Underwater Acoustic Sensor Network Using Mobile Anchor(s).

Underwater acoustic sensor networks (UWASNs) can revolutionize the subsea domain by enabling low-cost monitoring of subsea assets and the marine environment. Accurate localization of the UWASNs is essential for these applications. In general, range-based localization techniques are preferred for their high accuracy in estimated locations. However, they can be severely affected by variable sound speed, multipath spreading, and other effects of the acoustic channel. In addition, an inefficient localization scheme can consume a significant amount of energy, reducing the effective life of the battery-powered sensor nodes. In this paper, we propose robust, efficient, and practically implementable localization schemes for static UWASNs. The proposed schemes are based on the Time-Difference-of-Arrival (TDoA) measurements and the nodes are localized passively, i.e., by just listening to beacon signals from multiple anchors, thus saving both the channel bandwidth and energy. The robustness in location estimates is achieved by considering an appropriate statistical noise model based on a plausible acoustic channel model and certain practical assumptions. To overcome the practical challenges of deploying and maintaining multiple permanent anchors for TDoA measurements, we propose practical schemes of using a single or multiple surface vehicles as virtual anchors. The robustness of localization is evaluated by simulations under realistic settings. By combining a mobile anchor(s) scheme with a robust estimator, this paper presents a complete package of efficient, robust, and practically usable localization schemes for low-cost UWASNs.

Leveraging Digital Twin for Sustainability Assessment of an Educational Building

The EU Green Deal, beginning in 2019, promoted a roadmap for operating the transition to a sustainable EU economy by turning climate issues and environmental challenges into opportunities in all policy areas and making the transition fair and inclusive for all. Focusing on the built environment, the voluntary adoption of rating systems for sustainability assessment is growing, with an increasing market value, and is perceived as a social responsibility both by public administration and by private companies. This paper proposes a framework for shifting from a static sustainability assessment to a digital twin (DT)-based and Internet of Things (IoT)-enabled dynamic approach. This new approach allows for a real-time evaluation and control of a wide range of sustainability criteria with a user-centered point of view. A pilot building, namely, the eLUX lab cognitive building in the University of Brescia, was used to test the framework with some sample applications. The educational building accommodates the daily activities of the engineering students by constantly interacting with the sensorized asset monitoring indoor comfort and air quality conditions as well as the energy behavior of the building in order to optimize the trade-off with renewable energy production. The framework is the cornerstone of a methodology exploiting the digital twin approach to support the decision processes related to sustainability through the whole building’s life cycle.

Industrial Internet of Learning (IIoL): IIoT based pervasive knowledge network for LPWAN\u2014concept, framework and case studies

Industrial Internet of Things (IIoT) is performed based on the multiple sourced data collection, communication, management and analysis from the industrial environment. The data can be generated at every point in the manufacturing production process by real-time monitoring, connection and interaction in the industrial field through various data sensing devices, which creates a big data environment for the industry. To collect, transfer, store and analyse such a big data efficiently and economically, several challenges have imposed to the conventional big data solution, such as high unreliable latency, massive energy consumption, and inadequate security. In order to address these issues, edge computing, as an emerging technique, has been researched and developed in different industries. This paper aims to propose a novel framework for the intelligent IIoT, named Industrial Internet of Learning (IIoL). It is built using an industrial wireless communication network called Low-power wide-area network (LPWAN). By applying edge computing technologies in the LPWAN, the high-intensity computing load is distributed to edge sides, which integrates the computing resource of edge devices to lighten the computational complexity in the central. It cannot only reduce the energy consumption of processing and storing big data but also low the risk of cyber-attacks. Additionally, in the proposed framework, the information and knowledge are discovered and generated from different parts of the system, including smart sensors, smart gateways and cloud. Under this framework, a pervasive knowledge network can be established to improve all the devices in the system. Finally, the proposed concept and framework were validated by two real industrial cases, which were the health prognosis and management of a water plant and asset monitoring and management of an automobile factory.

Последние тенденции в развитии подводной беспроводной сенсорной сети: систематический обзор литературы

Underwater Wireless Sensor Networks (UWSNs) is an emerging technology for the monitoring of aquatic assets and frequently applied in several domains like underwater information gathering, ocean sampling network, anonymous vehicles, disaster prevention and submarine detection. Recently, UWSNs have been getting significant attention of researchers from both academia and industry. As a result, several studies have been carried out to perform certain improvements in UWSNs techniques, tools, protocols and architectures. In this regard, there is a dire need to investigate and summarize the modern UWSNs trends altogether within a single study. To achieve this, a Systematic Literature Review (SLR) is performed in this article to comprehensively analyze the latest developments in UWSNs. Particularly, 34 research studies published during 2012-2020 have been selected and examined in the area of UWSNs. This leads to the identification of 21 modern routing protocols and 11 tools. Furthermore, 5 different architecture types and 3 communication media technologies are presented in the context of UWSNs. Finally, a comparative analysis of routing protocols is done on the basis of important evaluation metrics. It has been concluded that there exist adequate approaches, protocols and tools for the monitoring of UWSNs. However, the design verification capabilities of existing approaches are insufficient to meet the growing demands of UWSNs. The findings of this article provide solid platform to enhance the current UWSNs tools and techniques for large and complex networks.

Condition monitoring of critical industrial assets using high performing low-cost MEMS accelerometers

The high investment cost, which is in majority introduced by the sensor costs, is one of the barriers for adopting Predictive Maintenance/Condition-Based Maintenance strategy in the industry. To lower this barrier, the use of cost-effective sensors for such an application is, therefore, a necessity. This paper presents experiences in setting-up two different remote vibration monitoring systems using low-cost MEMS accelerometers available on the market in two different industrial settings. Some technical challenges and the state-of-the-art algorithms used to analyze the measured raw vibration signals are presented. The installed vibration monitoring systems have successfully detected faults on two different critical assets, namely an induction motor and an expander machine.