Asset Condition Monitoring Research Articles

An asset management modelling framework for wind turbine blades considering monitoring system reliability

By incorporating information about asset condition from a monitoring system, engineers can utilize asset management models to manage maintenance activities on wind turbine blades throughout their lifespan. This can lower operating and maintenance costs and increase the life of the blades. The asset management model relies on the monitoring system as a source of information, however, commonly the reliability of the monitoring system is not considered. This paper presents a wind turbine blade asset management Petri net (PN) model that covers the blade asset management process, including degradation, inspection, condition monitoring (CM), and maintenance processes. The paper proposes two contributions. Firstly, while taking into account detailed industry guidelines, the developed model can forecast the future blade condition for a given asset management strategy. Secondly, it investigates the impact of the reliability of the monitoring system on the asset management modelling results. With the aid of the developed model, the number of repair actions and probability distributions of blade condition discovery time are obtained. In addition, the PN gives an indication of how misreporting (underestimation and overestimation) occurs and the extent of the misreporting. The simulation results illustrate the degree of uncertainty introduced into the monitoring results by the reliability of the monitoring system and, consequently, the extent to which this factor influences the maintenance strategies. The proposed model can be used to support asset management decisions when monitoring system performance degrades.

Implementasi Sistem Informasi Manajemen Aset Kelompok Ternak Ikan Hias Sumedang Menggunakan Metode Prototype

This study develops and implements an Asset Management Information System for Ornamental Fish Breeding Groups in Sumedang using the prototype method. The objective is to streamline the management of asset data, inventory, and asset condition monitoring in an efficient and organized manner. The prototype method was selected because it promotes intensive interaction between developers and users, enabling quick identification and resolution of needs and issues. The development stages include communication, planning, design modeling, prototype creation, and system implementation. This system features the ability to record and monitor assets, track asset loans, and log asset maintenance, thereby generating accurate reports and minimizing data errors. It is expected that this information system will help ornamental fish breeding groups in Sumedang manage their assets more effectively, boost productivity, and support the sustainability of their business.

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders.

The production of multivariate time-series data facilitates the continuous monitoring of production assets. The modelling approach of multivariate time series can reveal the ways in which parameters evolve as well as the influences amongst themselves. These data can be used in tandem with artificial intelligence methods to create insight on the condition of production equipment, hence potentially increasing the sustainability of existing manufacturing and production systems, by optimizing resource utilization, waste, and production downtime. In this context, a predictive maintenance method is proposed based on the combination of LSTM-Autoencoders and a Transformer encoder in order to enable the forecasting of asset failures through spatial and temporal time series. These neural networks are implemented into a software prototype. The dataset used for training and testing the models is derived from a metal processing industry case study. Ultimately, the goal is to train a remaining useful life (RUL) estimation model.

Transportation bridge infrastructure asset condition monitoring using uncrewed aerial vehicles (UAVs)

The optimal performance of a road transportation network depends on the serviceability and reliability of bridges connecting the pavements. Bridge performance depends on several geotechnical assets among which abutments play a key role as they support the bridge span, and transfer the vertical and horizontal loads from the superstructure to the foundation. Soil abutments are designed to be stable against overturning and sliding and prevent differential settlement and excessive lateral movements. In the United States, 42 % of bridges are observed to be over 50 years old. Department of Transportation (DOT) agencies periodically monitor and assess the bridge conditions typically every two years. Due to the costs and the time involved in conducting inspections using traditional data collection tools, DOTs are exploring innovative technologies to conduct inspections and realize benefits in terms of access, cost, and safety. They have widely adopted the use of uncrewed aerial vehicles (UAVs) for structural health monitoring of bridges. A few studies also conducted 360⁰ bridge inspections using camera sensors mounted on UAVs in the recent past. In this current study, the US Federal Aviation Administration (FAA) guidelines for commercial use of drones were discussed and followed in field studies. Further, UAVs were demonstrated for conducting 360° bridge inspections and identifying the erosion near abutments using three-dimensional models. This paper presents the application of innovative technologies for geotechnical assessments of two bridges in the Alaska region, with a focus on assessing the need for rehabilitation measures. These new technologies have become essential for reliable condition monitoring post-disaster events as well. This approach is expected to benefit the agencies in managing the transportation geotechnical assets in a timely and cost-effective manner.

Structured Data Ontology for AI in Industrial Asset Condition Monitoring

This paper proposes an ontology for prognostics and health management (PHM) applications involving sensor networks monitoring industrial machinery. Deep learning methods show promise for the development of autonomous PHM systems but require vast quantities of structured and representative data to realize their potential. PHM systems involve unique and specialized data characterized by time and context, and thus benefit from tailored data management systems. Furthermore, the use of dissimilar standards and practices with respect to database structure and data organization is a hinderance to interoperability. To address this, this paper presents a robust, structured data ontology and schema that is designed to accommodate a wide breadth of PHM applications. The inclusion of contextual and temporal data increases its value for developing and deploying enhanced ML-driven PHM systems. Challenges around balancing the competing priorities of structure and flexibility are discussed. The proposed schema provides the benefits of a relational schema with some provisions for noSQL-like flexibility in areas where PMH applications demand it. The selection of a database engine for implementation is also discussed, and the proposed ontology is demonstrated using a Postgres database. An instance of the database was loaded with large auto-generated fictitious data via multiple Python scripts. CRUD (create, read, update, delete) operations are demonstrated with several queries that answer common PHM questions.

An Environmentally Adaptive and Contrastive Representation Learning Method for Condition Monitoring of Industrial Assets.

Condition monitoring of assets is significant to the efficiency and reliability of industrial automation systems. However, the accuracy of condition monitoring results is easily impaired by variational environments and volatile operations, especially for complex automation systems. In this article, an environmentally adaptive and contrastive representation learning method is proposed to address the problem. To suppress the unexpected effects of environmental variations on operating data, a regression model between the operational and environmental variables is developed. The variable regression adjustment is achieved by solving a penalized optimization problem based on spline functions, and the solution is explicitly derived. Then, negative samples and pseudo labels are generated based on the designed pattern of data augmentation, and valid data representations for asset condition monitoring can be obtained by contrastive learning. Moreover, the reference statue of healthy assets is established by kernel density estimation, and control charts are employed for online monitoring with alarm thresholds. Taking wind turbine blades as examples, the remarkable performance of the developed method is demonstrated with real-world measurements from wind farms. Furthermore, comparative analysis with benchmark approaches and ablation study are conducted to reveal the superiority and effectiveness of the proposed method.

Threat Modeling for Communication Security of IoT-Enabled Digital Logistics

The modernization of logistics through the use of Wireless Sensor Network (WSN) Internet of Things (IoT) devices promises great efficiencies. Sensor devices can provide real-time or near real-time condition monitoring and location tracking of assets during the shipping process, helping to detect delays, prevent loss, and stop fraud. However, the integration of low-cost WSN/IoT systems into a pre-existing industry should first consider security within the context of the application environment. In the case of logistics, the sensors are mobile, unreachable during the deployment, and accessible in potentially uncontrolled environments. The risks to the sensors include physical damage, either malicious/intentional or unintentional due to accident or the environment, or physical attack on a sensor, or remote communication attack. The easiest attack against any sensor is against its communication. The use of IoT sensors for logistics involves the deployment conditions of mobility, inaccesibility, and uncontrolled environments. Any threat analysis needs to take these factors into consideration. This paper presents a threat model focused on an IoT-enabled asset tracking/monitoring system for smart logistics. A review of the current literature shows that no current IoT threat model highlights logistics-specific IoT security threats for the shipping of critical assets. A general tracking/monitoring system architecture is presented that describes the roles of the components. A logistics-specific threat model that considers the operational challenges of sensors used in logistics, both malicious and non-malicious threats, is then given. The threat model categorizes each threat and suggests a potential countermeasure.

Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review

The high-voltage (HV) assets in the existing power transmission network will experience increased electrical, thermal, environmental and mechanical stresses and, therefore, robust condition monitoring is critical for power system reliability planning. Fibre Bragg grating (FBG) sensors offer a promising technology in HV applications due to their immunity to electromagnetic interference and multiplexing capability. This paper reviews the current technology readiness levels of FBG sensors for condition monitoring of transformers, transmission lines, towers, overhead insulators and power cables, with the aim of stimulating further development and deployment of fibre-based HV asset management systems. Currently, there are several reported cases of FBG sensors used for condition monitoring of HV assets in the field, proving their feasibility for long-term use in the power grid. The review shows that FBG technology is versatile and can facilitate multi-parameter measurements, which will standardise the demodulation equipment and reduce challenges with integrating different sensing technologies.

Reverse Blocking Over Current Busbar Protection Scheme Based on IEC 61850 Architecture

Substation Automation System (SAS) is currently in a matured state of technology that shall facilitate transformational changes from conventional protection scheme. IEC 61850 protocol is considered as the crux of digital SAS due to its multifunction features that include seamless communication, ability to integrate various intelligent electronic devices, potential for improved real-time condition monitoring, reliable protection, and control of critical electrical assets. Because the application of IEC 61850 in SAS is relatively new and has not fully implemented in many substations yet, further feasibility studies using multivendor equipment to assess its performance under different operating conditions is imperative. In this article, a practical reliable and efficient reverse blocking over current bus bar protection scheme based on IEC 61850 is implemented and tested. Also, a comparison of digital SAS and conventional protection scheme is presented to highlight the superiority of the former one. Experimental results attest the reliability and effectiveness of the proposed digital protection scheme along with the accuracy and security of transmitting data packets using sampled values and generic objective-oriented substation event communication protocols adopted by IEC 61850.

Analysis of Variational Autoencoders for Imputing Missing Values from Sensor Data of Marine Systems

Of all the causes of accidents to ships, 14% pertains to damage due to ship equipment. Accordingly, the maritime industry is currently considering state-of-the-art maintenance and inspection processes, an example of which is condition-based maintenance (CBM). This is a strategy that hinges on the condition monitoring (CM) of assets. CM has proven to increase efficiency, reliability, profitability, and performance of vessel. To enable this maintenance strategy, sensors need to be installed along the most critical ship components and around the environment where these assets are operating through the application of Internet of Ships (IoS). IoS has demonstrated to be effective for collecting data in real time as well as performing diagnosis and prognosis to assess the current and future health of machinery to assist instant decision-making. The employment of IoS presents several challenges, an example of which is the imputation of missing values. Data imputation is a compelling preprocessing step, the aim of this is to estimate identified missing values to avoid underutilization of data. This data preparation step has gained popularity over the last few years due to its importance when dealing with Industrial Internet of Things (IIoT) sensor data. Although some articles presented new methodologies to impute missing values from sensor data of marine machinery based on machine learning methodologies, deep learning models have not yet been considered. For this reason, variational autoencoders for imputing missing values from sensor data of marine systems are analyzed in this article. To assess the performance of variational autoencoders as imputation methods, a comparative study is performed with widely implemented imputation techniques. Mean imputation, Forward Fill and Backward Fill, and k-Nearest Neighbors are considered. To that end, a case study on marine machinery system parameters obtained from sensors installed on a diesel generator of a tanker ship is performed. Results demonstrate the applicability of variational autoencoders when dealing with missing values of marine machinery systems sensor data, achieving a coefficient of determination of 0.99 when imputing missing values of the diesel generator power parameter.

Application of Unmanned Aerial Technologies for Inspecting Pavement and Bridge Infrastructure Assets Conditions

Infrastructure in the U.S.A. was graded with a “C–” in the 2021 ASCE report card. Frequent monitoring of infrastructure assets is key to ensuring the serviceability of the assets and safety of public users. Advancements in aerial technologies, compact sensors, computer processing, and image analysis software have given rise to various engineering applications of sensor-mounted unmanned aerial vehicle (UAV) platforms. Several contactless sensors are being mounted on UAVs to conduct a comprehensive assessment of infrastructure conditions compared to a qualitative visual examination by an experienced inspector. Many transportation agencies in the U.S.A. have been using UAVs for applications ranging from the research stage to regular inspection activities. These applications are mainly focused on inspecting infrastructure assets such as pavements, bridges, substructures, railways, and other assets. The current research discussed the approaches followed to conduct aerial condition monitoring of transportation infrastructure assets using optical sensors mounted on UAVs. Qualitative and quantitative inspections of pavement, bridge, and substructure infrastructure case studies were performed. Pavement distress extents were measured in three dimensions and a 360° inspection of bridges was conducted by accessing hard-to-reach areas. Scaled views of the four sides of a bridge were developed and the challenges in data collection and processing were outlined. Further, a localized inspection was also demonstrated to show the feasibility of using photogrammetry for remote condition assessments post-disaster/emergency. Overall, the rich visualization, safety, flexibility, and ease of handling offered by these technologies are expected to transform the method of conducting infrastructure performance monitoring inspections in the future.

After-Sales Service Contracting: Condition Monitoring and Data Ownership

Problem definition: Condition monitoring (CM) of durable assets, whereby sensors continuously monitor the health of an asset, is heralded as a key application of the Internet of Things. However, questions about ownership of the sensor data are seen as a key barrier to adoption. We model an after-sales supply chain in which the asset manufacturer provides maintenance and repair services to a customer that operates the asset. The asset condition deteriorates in a stochastic fashion and will eventually fail if not repaired. Methodology/results: We analyze a performance-based contracting problem considering manufacturer maintenance effort (the condition at which preventive maintenance is performed) and customer operating effort (which reduces the rate of condition deterioration). With information asymmetry on the customer’s effort cost, we analyze this contracting problem in a principal-agent model with double moral hazard. In the centralized setting, we establish that the benefit of CM increases and then decreases in the asset’s deterioration rate and that CM may increase or decrease the benefit of customer effort depending on the deterioration rate. In the decentralized setting, we prove that CM always benefits the manufacturer and the supply chain, but it may hurt the customer if the asset reliability is sufficiently high. Managerial implications: These results have important implications for the effect of sensor-data ownership. The manufacturer will adopt CM if it owns the data, but the customer may block CM adoption if it owns the data. We show that this CM adoption barrier can be overcome by the manufacturer offering to pay an appropriate data access fee. However, under this arrangement, the manufacturer may not benefit from a more-effective customer operating effort. We discuss the resulting implication on the manufacturer’s product design and the business model between selling and leasing.

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.

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.

An expert system for EMI data classification based on complex Bispectrum representation and deep learning methods

This paper presents expert system framework based on Machine Learning (ML) for High-Voltage (HV) asset condition monitoring. The work investigates the classification of insulation faults in HV environment, based on real-world time series signals labelled by condition monitoring experts. Extending on our previous work, the proposed approach exploits the Bispectrum analysis and deep learning for feature extraction and classification. The calculated Bispectrum on time series signals can be deployed as the complex-valued Bispectrum, which contains phase information, or as its real-valued magnitude. This can be approached as an image classification problem which can be implemented in various deep networks including Convolutional Neural Network (CNN), Residual Neural Network (ResNet) and their complex-valued version. The employed deep networks performance is compared in terms of their classification accuracy. High classification performance is obtained which produces comparable performance with expert diagnosis. Thus, it can be interpreted as transfer of expert system to an intelligent system.

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.

The Role of Artificial Intelligence (AI) in Condition Monitoring and Diagnostic Engineering Management (COMADEM): A Literature Survey

Artificial Intelligence (AI) is playing a dominant role in the 21st century. Organizations have more data than ever, so it’s crucial to ensure that the analytics team should differentiate between Interesting Data and Useful Data. Amongst the important aspects in Machine Learning are “Feature Selection” and “Feature Extraction”. We are now witnessing the emerging fourth industrial revolution and a considerable number of evolutionary changes in machine learning methodologies to achieve operational excellence in operating and maintaining the industrial assets efficiently, reliably, safely and cost-effectively. AI techniques such as, knowledge based systems, expert systems, artificial neural networks, genetic algorithms, fuzzy logic, case-based reasoning and any combination of these techniques (hybrid systems), machine learning, biomimicry such as swarm intelligence and distributed intelligence. are widely used by multi-disciplinarians to solve a whole range of hitherto intractable problems associated with the proactive maintenance management of industrial assets. In this paper, an attempt is made to review the role of artificial intelligence in condition monitoring and diagnostic engineering management of modern engineering assets. The paper also highlights that unethical and immoral misuse of AI is dangerous.

Intelligent Diagnostics for Ball Screw Fault Through Indirect Sensing Using Deep Domain Adaptation

Intelligent data-driven fault diagnostics for rotating machinery is well established. However, ball screws pose a unique challenge of impractical sensor locations for long-term deployment due to their complex motion trajectory and sophisticated mechanical structure. To overcome this challenge, an indirect sensing method is proposed. While techniques are available for multiple transfer learning tasks, cross-sensor domain adaptation remains unexplored. Thus, a convolutional neural network-based domain adaptation method is proposed that minimizes the maximum mean discrepancy of high-level representations between domains and exploits novel parallel data to attain class-level alignment. Proposed method achieved a mean testing accuracy of 98.25% upon validation on 33 transfer tasks designed across five accelerometer locations on a ball screw testbed depicting nine health conditions through variations in preload levels and backlash. This convenience of transferability of the diagnostic model between sensor locations can go a long way in robust and reliable condition monitoring of critical assets.

Sensing system for low cost condition monitoring of remote assets

Local councils, power and road agencies have tens of thousands of assets such as road signs, power poles, and public artworks. Asset management plans dictate that these assets need to be inspected periodically, for example once per year. In between inspections the assets are vulnerable to random events such as weather and adverse human interactions. Asset managers need to know when these events occur, as damage to these assets can impact public safety. Traditional instrumentation for this high volume, low asset value application has been too costly to install. This paper presents a low cost design and associated edge-processing strategy to enable low-power transmission of a vibration signal notifying of change in state of an asset. The design is demonstrated using a COTS (commercial off-the-shelf) microcontroller, MEMS (microelectromechanical system) accelerometer and a LoRa (Long Range spread spectrum modulation technique) module. We name our device RAMI (Remote Asset Management Indicator). The design draws less than 1 mA in normal operation, lasts over a year between battery changes, costs less than AU$100 (US$65) to construct and can transmit around 400 metres in an urban setting and up to 3 km in a rural setting. The open-source design and code are available for download.

Integrated monitoring system to assess LV flexibility impact

This study describes the strategic benefits for Electronic Data Processing (EDP) Distribution of implementing an integrated monitoring system in the low-voltage (LV) side of secondary substations. This system means a step forward in the digitalisation of the LV grid which acts in conjunction with on-going smart grid appliances, growing up synergies between them and allowing better visibility in this part of the grid. The monitoring system consists of the measurement of each LV circuit, integrating and processing the data in an online platform. With these measurements, along with smart grid data, there are proposed four use cases: (i) LV asset condition monitoring; (ii) network power quality; (iii) grid topology identification; (iv) energy balance. This study sums up the highlights and initial conclusions of testing this solution for almost a year, including specific results for each use case, benefits and future recommendations for adopting this solution as a rollout.