Condition-based Approach Research Articles

Adaptive Model Compression for Steel Plate Surface Defect Detection: An Expert Knowledge and Working Condition-Based Approach

Adaptive Model Compression for Steel Plate Surface Defect Detection: An Expert Knowledge and Working Condition-Based Approach

Assessing the economic value of Posidonia oceanica (L.) at Tremiti Islands (Mediterranean Sea): An ecosystem condition-based approach

In the context of limiting global warming, the seagrass Posidonia oceanica (L.) gained the centrality of several international climate change mitigation projects being the most effective carbon storage sink among Mediterranean seagrasses. To assess and monitor the change of environmental conditions and economic values of natural resources, the present study moves from the insights of the System of Environmental-Economic Accounting – Ecosystem Accounting to assess the economic value of the carbon sequestration and storage capacity of the Mediterranean-endemic seagrass P. oceanica at the Tremiti Islands Marine Protected Area. The economic value is compared across: i. the reference study by Pergent-Martini et al.; ii. the ecological condition-based approach; and iii. the unit value transfer. Based on the obtained outcomes, an ecosystem-based approach would prevent biases in the accounting of the ecosystem-service provision capacity of P. oceanica and help the policy maker to implement adequate public investment policies to mitigate its overall degradation.

Genetic testing in cancer care: considerations for a condition-based approach.

Genetic testing in cancer care: considerations for a condition-based approach.

Optimal maintenance management for synchronous generators: A condition-based approach to predict and assess equipment wear

Optimal maintenance management for synchronous generators: A condition-based approach to predict and assess equipment wear

Review of maintenance strategies for ship machinery systems

This study presents the state-of-art and future trends of the maintenance approaches to ship machinery systems. A systematic review process is conducted to identify relevant literature. Firstly, literature is examined by bibliometric analyses based on the determination of the distribution of papers by years, the top authors, papers, journals, and organisations. Then, studies in the literature are reviewed and presented. In addition, the advantages and difficulties of each maintenance strategy are discussed. Finally, an effective maintenance policy that can implement in new ship concepts such as autonomous marine vessels is introduced in line with the information from the literature. In the future, it is believed that maintenance practices will be performed via the condition-based approach when the evolution of followed maintenance operations in ship machinery systems is examined. However, it should be underlined that more effective and powerful maintenance management can be achieved by supporting the system performance monitoring with reliability and risk approaches.

Validation of a Novel Sensing Approach for Continuous Pavement Monitoring Using Full-Scale APT Testing

The objective of this paper is to present a novel approach for the continuous monitoring of pavement condition through the use of combined piezoelectric sensing and novel condition-based interpretation methods. The performance of the developed approach is validated for the detection of bottom-up fatigue cracking through full-scale accelerated pavement testing (APT). The innovative piezoelectric sensors are installed at the bottom of a thin 102 mm (4 in.) asphalt layer. The structure is then loaded until failure (up to 1 million loading cycles in this study). The condition-based approach, used in this work, does not rely on stain measurements and allows users to bypass the need for any structural or finite-element models. Instead, the data compression approach relies on variations in strain energy harvested by smart sensors to track changes in material and structural conditions. Falling weight deflectometer (FWD) measurements and visual inspections were used to validate the observations from the sensing system. The results in this paper present a first large-scale validation in pavement structures for a piezopowered sensing system combined with a new response-only based approach for data reduction and interpretation. The proposed data analysis method has demonstrated a very early detection capability compared to classical inspection methods, which unveils a huge potential for improved pavement monitoring.

Demand response for frequency regulation with neural network load controller under high intermittency photovoltaic systems

The rate of change of frequency (ROCOF) has become a key parameter to be monitored under high penetration of renewable energy. Any significant ROCOF should be mitigated immediately to avoid any shutdown of power plants and hence power interruptions to the customers. However, ROCOF is found to be significant and often in a region near to equatorial line because the power output of PV is highly intermittent due to a large number of passing clouds. Demand response (DR) is one of the most cost-effective means for mitigating any high ROCOF through controlling heating elements, refrigerators, and air-conditioners. However, most of the DR controllers use pre-determined conditions, also known as the condition-based approach, to switch the loads for reducing frequency changes. This approach may not be effective enough to respond to a new load condition. Furthermore, many DR controllers, including those accompanied by machine learning, are mainly developed to mitigate frequency changes with little focus on the mitigation of high ROCOF. Hence, an artificial neural network (ANN)-based controller is proposed in this article for frequency regulation with auto corrective efforts on high ROCOF under high intermittency of PV systems. The proposed controller is designed to manage several controllable loads on a grid emulator with PV systems at Universiti Tunku Abdul Rahman, Malaysia. It is shown that the proposed controller can reduce the frequency deviation by 23% and improve ROCOF by 19.7% as compared to that without any controller.

A Sensory Feedback Based Discrete Distributed Observer to Cooperative Output Regulation

The discrete distributed observer is developed recently to solve the cooperative output regulation problem. The validation of the existing discrete distributed observer depends on the establishment of the communication network. In some practical applications, only the sensor network can be established among all agents. To handle such cases, we propose a sensory feedback-based discrete distributed observer, which depends on the relative output of neighboring agents. In contrast to existing results, the design of the discrete distributed observer and cooperative output regulation analysis should be conducted simultaneously. Resorting to the small-gain theorem, we develop a small-gain condition-based approach and a low-gain-based approach, respectively. By these two approaches, we demonstrate that the discrete distributed observer holds and the cooperative output regulation problem of heterogeneous discrete-time linear multiagent systems can be solved.

Online-monitoring of motor operated valves

Abstract Traditionally, evaluating the running behaviour of motor operated valves (MOV) is based upon a non-continuous and demand oriented method. Gundremmingen Nuclear Power Plants has improved this strategy over the last 3 years. At MOVs within the safety system, diagnostics modules were installed for an online monitoring of active power during every run on demand. In the near future, functionality will be further enhanced in order to increase the particular knowledge, as a basis for both a safety oriented, but optimised, condition-based preventive maintenance approach and a plant life management strategy. Furthermore, during spring outage of unit B in 2002, a benchmark was implemented at four MOVs, which includes both comparison between two different kinds of power monitoring as well as active control of one of the systems.

Automated Condition-Based Suppression of the CPR Artifact in ECG Data to Make a Reliable Shock Decision for AEDs during CPR.

Cardiopulmonary resuscitation (CPR) corrupts the morphology of the electrocardiogram (ECG) signal, resulting in an inaccurate automated external defibrillator (AED) rhythm analysis. Consequently, most current AEDs prohibit CPR during the rhythm analysis period, thereby decreasing the survival rate. To overcome this limitation, we designed a condition-based filtering algorithm that consists of three stop-band filters which are turned either ‘on’ or ‘off’ depending on the ECG’s spectral characteristics. Typically, removing the artifact’s higher frequency peaks in addition to the highest frequency peak eliminates most of the ECG’s morphological disturbance on the non-shockable rhythms. However, the shockable rhythms usually have dynamics in the frequency range of (3–6) Hz, which in certain cases coincide with CPR compression’s harmonic frequencies, hence, removing them may lead to destruction of the shockable signal’s dynamics. The proposed algorithm achieves CPR artifact removal without compromising the integrity of the shockable rhythm by considering three different spectral factors. The dataset from the PhysioNet archive was used to develop this condition-based approach. To quantify the performance of the approach on a separate dataset, three performance metrics were computed: the correlation coefficient, signal-to-noise ratio (SNR), and accuracy of Defibtech’s shock decision algorithm. This dataset, containing 14 s ECG segments of different types of rhythms from 458 subjects, belongs to Defibtech commercial AED’s validation set. The CPR artifact data from 52 different resuscitators were added to artifact-free ECG data to create 23,816 CPR-contaminated data segments. From this, 82% of the filtered shockable and 70% of the filtered non-shockable ECG data were highly correlated (>0.7) with the artifact-free ECG; this value was only 13 and 12% for CPR-contaminated shockable and non-shockable, respectively, without our filtering approach. The SNR improvement was 4.5 ± 2.5 dB, averaging over the entire dataset. Defibtech’s rhythm analysis algorithm was applied to the filtered data. We found a sensitivity improvement from 67.7 to 91.3% and 62.7 to 78% for VF and rapid VT, respectively, and specificity improved from 96.2 to 96.5% and 91.5 to 92.7% for normal sinus rhythm (NSR) and other non-shockables, respectively.

System of Water Vehicle Power Plant Remote Condition Monitoring

Remote condition monitoring of water vehicles plays an important role in preventing potentially very expensive marine incidents and ensuring maximum efficiency of a ship's operation and reliability with minimum maintenance downtime and repair costs. Concept of the condition-based approach to maintenance is today's best practise, and it is becoming increasingly important to move from planned maintenance to condition-based maintenance, to reduce the increasingly high cost of maintaining a modern fleet. Onboard and remote monitoring is now an essential part of condition-based maintenance process to obtain the good quality data, correct analysis, and effective counteractive actions necessary for such an approach, and article presents the water vehicle power plant monitoring model developed by authors. Considered approach, coupled with preventive maintenance, saves shipowners time and money through early diagnosis of component failure or excess wear. Power plant of water vehicle comprises far more than just an engine with its auxiliary equipment but also other main propulsion blocks – in particular, thrusters. The result was the development of the Water Vehicle Condition Monitoring (WVCM) system, which enables to closely examine water vehicle equipment performance. A WVCM system comprises the following installed onboard: accelerometers, pressure and temperature transmitters, oil, fuel and exhaust monitoring units and a torque measurement system.

Moving beyond land use intensity types: assessing biodiversity impacts using fuzzy thinking

PurposeThe impact of land use on biodiversity is a topic that has received considerable attention in life cycle assessment (LCA). The methodology to assess biodiversity in LCA has been improved in the past decades. This paper contributes to this progress by building on the concept of conditions for maintained biodiversity. It describes the theory for the development of mathematical functions representing the impact of land uses and management practices on biodiversity.MethodsThe method proposed here describes the impact of land use on biodiversity as a decrease in biodiversity potential, capturing the impact of management practices. The method can be applied with weighting between regions, such as ecoregions. The biodiversity potential is calculated through functions that describe not only parameters which are relevant to biodiversity, for example, deadwood in a forest, but also the relationships between those parameters. For example, maximum biodiversity would hypothetically occur when the nutrient balance is ideal and no pesticide is applied. As these relationships may not be readily quantified, we propose the use of fuzzy thinking for biodiversity assessment, using AND/OR operators. The method allows the inclusion of context parameters that represent neither the management nor the land use practice being investigated, but are nevertheless relevant to biodiversity. The parameters and relationships can be defined by either literature or expert interviews. We give recommendations on how to create the biodiversity potential functions by providing the reader with a set of questions that can help build the functions and find the relationship between parameters.Results and discussionWe present a simplified case study of paper production in the Scandinavian and Russian Taiga to demonstrate the applicability of the method. We apply the method to two scenarios, one representing an intensive forestry practice, and another representing lower intensity forestry management. The results communicate the differences between the two scenarios quantitatively, but more importantly, are able to provide guidance on improved management. We discuss the advantages of this condition-based approach compared to pre-defined intensity classes. The potential drawbacks of defining potential functions from industry-derived studies are pointed out. This method also provides a less strict approach to a reference situation, consequently allowing the adequate assessment of cases in which the most beneficial biodiversity state is achieved through management practices.ConclusionsThe originality of using fuzzy thinking is that it enables land use management practices to be accounted for in LCA without requiring sub-categories for different intensities to be explicitly established, thus moving beyond the classification of land use practices. The proposed method is another LCIA step toward closing the gap between land use management practices and biodiversity conservation goals.

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.

Life-cycle management cost analysis of transportation bridges equipped with seismic structural health monitoring systems

Life-cycle cost analysis is an approach that has gained popularity for assisting the design of civil infrastructures. The life-cycle cost analysis approach can be leveraged for structures equipped with structural health monitoring systems in order to quantify the benefits of the technology and de facto support its long-term implementation. However, for new structures, the long-term assessment of the expected value of the total investment cost, in terms of the current worth at the design time, is still the focus of ongoing research due to unknowns and uncertainties on the impact of the structural health monitoring system on long-term structural performance. This article proposes a new combined model of life-cycle cost formulation and simulation methodology for the long-term financial assessment of transportation bridges equipped with seismic structural health monitoring systems, in order to evaluate the total costs and benefits offered by such monitoring systems for post-seismic assessments. The formulation characterizes the time evolution of bridge management cost terms, highlighting the most sensitive parameters. The simulation methodology allows to quantitatively weigh each maintenance action on the total cost based on when the action is performed. The model is used to compare structures managed by the traditional approach of post-earthquake inspection versus those managed by a condition-based approach enabled by structural health monitoring systems. The originality of the model empowers the comparison by payback time, defined as the break-even point between costs and benefits of a structural health monitoring system, as well as by economic gain, defined as the difference between the total costs of an unmonitored versus a monitored structure through the end of service life. The proposed model is demonstrated through parametric analyses on a case study consisting of a continuous steel-concrete composite bridge, where the structural health monitoring system is used to monitor the elastic limit state condition of bending forces in piers during the earthquake.

Broadband signal reconstruction for SHM: An experimental and numerical time reversal methodology

Structural Health Monitoring (SHM) aims to shift aircraft maintenance from a time-based to a condition-based approach. Within all the SHM techniques, Acoustic Emission (AE) allows for the monitoring of large areas by analyzing Lamb waves propagating in plate like structures. In this study, the authors proposed a Time Reversal (TR) methodology with the aim of reconstructing an original and unaltered signal from an AE event. Although the TR method has been applied in Narrow-Band (NwB) signal reconstruction, it fails when a Broad-Band (BdB) signal, such as a real AE event, is present. Therefore, a novel methodology based on the use of a Frequencies Compensation Transfer Function (FCTF), which is capable of reconstructing both NwB and real BdB signals, is presented. The study was carried out experimentally using several sensor layouts and materials with two different AE sources: (i) a Numerically Built Broadband (NBB) signal, (ii) a Pencil Lead Break (PLB). The results were validated numerically using Abaqus/CAETM with the implementation of absorbing boundaries to minimize edge reflections.

Value-based optimisation for cross-asset maintenance in a Canadian municipality

Most municipalities have a disconnection between the effective usage of their budget and their ability to preserve the value of their network of assets. Long-term planning for cross-asset infrastructures is always a challenge for municipalities. This paper proposes a decision-making platform which optimises municipal assets by integrating cross-asset models with asset value to achieve an optimal solution for the maximum returns of investment over a long-term period. The method was compared with the classical condition-based optimisation approach by implementing it on a case study of the Municipality of Kindersley, Canada. It was found that the value-based optimisation model demonstrated meaningful results by integrating engineering concepts with the value of the assets to determine the optimal long-term investment planning. For the same available budget, the value-based model achieved a similar overall condition while increasing the total value relative to that of the condition-based approach. The life-cycle analysis showed that for 20 years’ investment in the case study, the value-based model obtained Can$18 million (US$13·5 million) more return, which validates the higher efficiency of the proposed model. The developed value-based optimisation technique enables municipalities to apply a multi-asset decision-making process that balances engineering and economic approaches to delivering better value for money.

Sustainable built asset management performance indicators and attributes

PurposeThis paper aims to identify key performance indicators (KPIs), and their corresponding attributes, required to successfully manage asset management sustainably in a built environment context. Improving the sustainability of existing housing stock is a major challenge facing the UK social housing sector. There is a lack of support to navigate the growing and often incongruent information relating to sustainable development and how to operationalise it. The problem is twofold; first, the current (single criterion) condition-based approach to maintenance planning constrains asset managers and does not fully address the social, environmental and economic aspects of sustainability. Second, the toolkits available for assessing the sustainability of housing are often generic and are time consuming and expensive to implement.Design/methodology/approachThis paper reports the findings of a participatory research project with a leading London-based housing association, using a series of landlord and tenant workshops to derive a set of attributes associated with KPIs to fully reflect the local requirements of the landlord and their interpretation of the sustainability agenda. Five KPIs are considered to be measurable, directly affected by maintenance work and independent of each other were identified by this landlord (comfort, running costs, adaptability, maintenance costs and community).FindingsThe resulting outputs, in a policy context, will provide a clear route map to social housing landlords of how to improve the sustainability of their housing stock with the additional benefits of addressing fuel poverty and carbon emission targets, whilst at the same time, help create and maintain housing in which people want to live.Originality/valueThe proposed approach is flexible enough to incorporate the individual requirements of landlords and be able to adapt to changes in government policy (local and central) in a timely, robust, transparent and inclusive format.

A Techno-Economic Centric Integrated Decision-Making Planning Approach for Optimal Assets Placement in Meshed Distribution Network Across the Load Growth

The modern distribution networks under the smart grid paradigm have been considered both interconnected and reliable. In grid modernization concepts, the optimal asset optimization across a certain planning horizon is of core importance. Modern planning problems are more inclined towards a feasible solution amongst conflicting criteria. In this paper, an integrated decision-making planning (IDMP) approach is proposed. The proposed methodology includes voltage stability assessment indices linked with loss minimization condition-based approach, and is integrated with different multi-criteria decision-making methodologies (MCDM), followed by unanimous decision making (UDM). The proposed IDMP approach aims at optimal assets sitting and sizing in a meshed distribution network to find a trade-off solution with various asset types across normal and load growth horizons. An initial evaluation is carried out with assets such as distributed generation (DG), photovoltaic (PV)-based renewable DG, and distributed static compensator (D-STATCOM) units. The solutions for various cases of asset optimization and respective alternatives focusing on technical only, economic only, and techno-economic objectives across the planning horizon have been evaluated. Later, various prominent MCDM methodologies are applied to find a trade-off solution across different cases and scenarios of assets optimization. Finally, UDM is applied to find trade-off solutions amongst various MCDM methodologies across normal and load growth levels. The proposed approach is carried out across a 33-bus meshed configured distribution network. Findings from the proposed IDMP approach are compared with available works reported in the literature. The numerical results achieved have validated the effectiveness of the proposed planning approach in terms of better performance and an effective trade-off solution across various asset types.

A condition-based dynamic segmentation of large systems using a Changepoints algorithm: A corroding pipeline case

Large structures are systems composed by a significant number of components with parallel or series distributions. How these components fail and interact, aggravate the complexity of the main structure reliability calculation. Some methods commonly proposed to reduce this complexity by dividing the system into segments of similar properties using dynamic or static approaches. Dynamic segmentations may depend on how aggressive is the structure’s surrounding conditions (e.g., the soil properties). Static segmentations could be given by fixed distances. However, in a few cases, these divisions follow a condition-based approach. This paper proposes an alternative dynamic segmentation to identify preliminary critical segments based on a Changepoint approach and data obtained from inspections. Changepoints algorithms have been used to determine changes in spatial measurements for a further reliability evaluation with appropriate limit state functions. This work focuses on onshore pipelines subjected to corrosion defects based on information obtained from In-Line Inspections (ILI). Onshore pipelines cross through a variety of soils, water corridors, and densely populated areas promoting spatial-dependent degradation processes like corrosion. ILI inspections are commonly used to identify the condition of the pipeline in terms of the remaining wall and location of metal loss at the inner and outer walls by using magnetic or ultrasonic instruments. Based on a burst failure limit state, the segments obtained with the changepoints approach are compared with a soil and static segmentations. The results indicate that the proposed approach could identify the main critical points of the pipeline using segments with statistical significance.

A Comprehensive Review of Condition Based Prognostic Maintenance (CBPM) for Induction Motor

This paper presents condition monitoring aspects of induction motor, its present status with possible mitigation schemes and future maintenance challenges. The induction motors constitute the major portion of motors in domestic and industrial applications. These motors experience different types of failures and faults associated with insulation, bearing, stator, rotor, and eccentricity. As a matter of fact, these faults may subsequently enhance the probability of failures unless proper introspection is not accomplished. In order to reduce the failure time and operating cost, early detection is indispensable which necessitates condition-based approach on contrary to scheduled maintenance. The condition monitoring is a strong candidate to address the diagnosis of machinery failure problems and unreliability. In this context, a comprehensive analysis is reported in the literature with a focus on different methodologies being addressed for such objective. Utmost efforts are made to comprehensively analyze in the reported literature in a sequential manner citing the advantage and limitations in this paper. The authors hopefully described and illustrated the associated problems with possible mitigation in the context of condition monitoring which would be immensely helpful for future researchers working in these aspects and the future roadmap would be clearly reflected.