Continuous Condition Monitoring Research Articles

Recent advancements in microfluidics that integrate electrical sensors for whole blood analysis.

Whole blood analysis reveals crucial information about various physiological and pathological conditions, including cancer metastasis, infection, and immune status, among others. Despite this rich information, the complex composition of whole blood usually required multiple sample preparation steps to purify targeted analytes. Traditionally, whole blood preparation processes, including centrifugation, lysis, dilution, or staining, are usually manually operated by well-trained technicians using bench-top instruments. This preparation can require a large blood volume and cannot be directly integrated with detection systems. Recently, various studies have integrated microfluidics with electrical sensors for whole blood analysis, with a focus on cell-based analysis, such as cell type, number, morphology, phenotype, and secreted molecules. These miniaturized systems require less sample and shorter reaction times. Besides, the sample processing and analysis can be fully integrated and automated with minimal operations. We believe these systems can transfer the current whole blood analysis from hospitals or laboratories into clinics or home settings to enable real-time and continuous health condition monitoring in point-of-care settings.

Digital Twin Enabled Asset Anomaly Detection for Building Facility Management

Assets play a significant role in building utilities by undertaking the majority of their service functionalities. However, a comprehensive facility management solution that can help to monitor, detect, record and communicate asset anomalous issues is till nowhere to be found. The digital twin concept is gaining increasing popularity in architecture, engineering and construction/facility management (AEC/FM) sector, and a digital twin enabled asset condition monitoring and anomaly detection framework is proposed in this paper. A Bayesian change point detection methodology is tentatively embedded to reveal the suspicious asset anomalies in a real time manner. A demonstrator on cooling pumps is developed and implemented based on Centre for Digital Built Britain (CDBB) West Cambridge Digital Twin Pilot. The results demonstrate that supported by the data management capability provided by digital twin, the proposed framework realizes a continuous condition monitoring and anomaly detection for single asset, which contributes to efficient and automated asset monitoring in O&M management.

Large Structural Shear Deformation and Failure Monitoring Using Bend Losses in Polymer Optical Fibre

Rapid identification of structural damage positions is essential to the post-disaster rehabilitation of structures and infrastructures. Large shear deformation, e.g., shear failure of bridge piers, shear-slip of slopes, and shear cracking of structural walls, is often the cause of structural instability. Distributed optical fibre sensing (DOFS) techniques have an advantage over point-based sensors in terms of spatial continuous structural condition monitoring. This paper presents the development of new measurement theory and algorithm to evaluate the structural shear deflection based on the large beam deflection and optical bend loss theories. The proposed technique adopted a photon-counting Optical Time Domain Reflectometer (ν-OTDR) with polymer optical fibres (POFs) which has a large deformation measurement range and high spatial resolution. In the experiment, shear deformation events can be successfully detected and evaluated from the proposed technique. When the normalised shear deformation is larger than 0.2, both the event locations and the magnitudes can be accurately determined. When normalised shear deformation is lesser than 0.2, the error in the magnitude evaluation increased, but the event location can be found with an absolute error <0.5 m. Multiple shear events can be treated as independent events when they are separated by more than 5 m. Various configurations of POFs attached to concrete beam specimens for rupture failure monitoring were also studied. The configuration that could maximise the POF curvature at the beam failure produced the largest ν-OTDR signals. In other configurations in which the POFs were only stretched at failure, the signals were less strong and were influenced by the POF-structure bonding strength.

Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.

Thermal condition monitoring of large smart bearing through fiber optic sensors

The ‘Smart Manufacturing’ applied to steelmaking includes a continuous condition monitoring of the mill system, performed by bearings equipped with sensors. They are embedded inside the mill cage, and exposed to heat sources. Monitoring their temperature is mandatory. Fiber optics sensors can be exploited. Their behavior is here modeled, and then tested on some prototypes. The fiber optics technology looks suitable for this application, provided that a precise sensor calibration is performed. A technological assessment is required, to develop the industrial product.

Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis

Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous condition monitoring of BRB faults demands a computationally efficient and accurate signal diagnosis technique. The advantage of high reliability and wide applicability in condition monitoring and fault diagnosis based on vibration signature analysis results in an improved cyclic modulation spectrum (CMS), which is one of the cyclic spectral analysis algorithms. CMS is proposed in this paper for the detection and identification of BRB faults in IMs at a steady-state operation based on a vibration signature analysis. The application of CMS is based on the short-time Fourier transform (STFT) and the improved CMS approach is attributed to the optimization of STFT. The optimal window is selected to improve the accuracy for identifying the BRB fault types and severities. The appropriate window length and step size are optimized based on the selected window function to receive a better calculation benefit through simulation and experimental analysis. Compared to other estimators, the improved CMS method provides better fault detectability results by analyzing vertical vibration signatures of a healthy motor, and damaged motors with 1 BRB and 2 BRBs under 0%, 20%, 40%, 60%, and 80% load conditions. Both synthetic and experimental investigations demonstrate the proposed methodology can significantly reduce computational costs and identify the BRB fault types and severities effectively.

Neural Network Controlled Primitive Fault Analysis and Monitoring of Wind Turbine Gear Box

The problem considered in this paper is minimization of operational and maintenance costs of Wind Energy Conversion Systems (WECS). A continuous condition monitoring system is to be designed for reducing these costs. Hence preliminary identification of the degeneration of the generator health, facilitating a proactive response, minimizing downtime, and maximizing productivity is made possible. The inaccessibility of Wind generators situated at heights of 30m or more height also creates problem in condition monitoring and fault diagnosis. This opens up the research on condition monitoring and fault diagnosis in WECS (blades, drive trains, and generators). Therefore different type of faults, their generated signatures, and their diagnostic schemes are discussed in this paper. The paper aims in validating the application of neural networks for the analysis of wind turbine data, so that possible future failures may be predicted and rectified earlier.

Toward Cloud-Assisted Industrial IoT Platform for Large-Scale Continuous Condition Monitoring

Process industries cover a wide set of industries, in which the processes are controlled by a combination of distributed control systems (DCSs) and programmable logic controllers (PLCs). These control systems utilize various measurements such as pressure, flow, and temperature to determine the state of the process and then use field devices such as valves and other actuating devices to manipulate the process. Since there are many different types of field devices and since each device is calibrated to its specific installation, when monitoring devices, it is important to be able to transfer not only the device measurement and diagnostics but also characteristics about the device and the process in which it is installed. The current monitoring architecture, however, creates challenges for continuous monitoring and analysis of diagnostic data. In this paper, we present the design of an Industrial Internet-of-Things (IIoT) system for supporting large-scale and continuous device condition monitoring and analysis in process control systems. The system design seamlessly integrates existing infrastructure [e.g., highway addressable remote transducer (HART) and WirelessHART networks, and DeltaV DCS] and newly developed hardware/software components (e.g., one-way data diode and IoT cellular architecture) together for control network data collection and streaming of the collected device diagnostic parameters to a private cloud to perform streaming data analytics designed for fault identification and prediction. A prototype system has been developed and supported by Emerson Automation Solutions and deployed in the field for design validation and long-term performance evaluation. To the best of our knowledge, this is the first ever publicly reported effort on IoT system design for process automation applications. The design can be readily extended for condition monitoring and analysis of many other industrial facilities and processes.

Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey

It has been over two decades since the publication of pioneer works about the power transformer diagnostics based on monitoring of their acoustic fingerprints. Since then, there has been great progress in this field and the methods used are as complex as ever. Any unnecessary intervention on a power transformer implies its temporary disconnection from the power grid. The inability to supply electricity to the customer means not only financial loss for the utility but also generates a non-material loss, e.g., the loss of reputation to the customer. Faster, more accurate, more reliable, and less invasive diagnosis is the main reason behind development and improvement in this field. The main goal of this paper is to categorize and review state-of-the art of vibro-acoustic diagnostic methods for power transformers. This paper opens with a brief note about continuous condition monitoring, after which we overview the causes of transformer vibrations as well as the collection and preprocessing of diagnostic data. Then, we review and categorize works related to the acoustic condition assessment of power transformers considering both: feature extraction in the time, frequency, time-frequency domain, and mathematical modeling and system identification of dynamic systems.

Life-cycle Assessment on the Highway Street Lantern using Weibull Probability Approach

Predictive maintenance (PM) evaluates the condition of equipment by performing periodic or continuous equipment condition monitoring. This paper presents a Weibull statistical approach, in order to determine life cycle of highway street lighting. The historical data that collected from the maintenance checklist record of 250 and 400W street lighting lanterns replacement for 2 years of period were used. Than, two key performance indicators which shape factor, β and characteristic life, η were determined to perform the Weibull probability analysis. The findings show that estimation of mean time to failure (MTTF) for 250 and 400 watt bulb are 4009.30 and 5043.36 hours period of operation respectively.

Continuous Condition Monitoring for High-power DC Charging Cables

Continuous Condition Monitoring for High-power DC Charging Cables

Contact-Lens Biosensors.

Rapid diagnosis and screening of diseases have become increasingly important in predictive and preventive medicine as they improve patient treatment strategies and reduce cost as well as burden on our healthcare system. In this regard, wearable devices are emerging as effective and reliable point-of-care diagnostics that can allow users to monitor their health at home. These wrist-worn, head-mounted, smart-textile, or smart-patches devices can offer valuable information on the conditions of patients as a non-invasive form of monitoring. However, they are significantly limited in monitoring physiological signals and biomechanics, and, mostly, rely on the physical attributes. Recently, developed wearable devices utilize body fluids, such as sweat, saliva, or skin interstitial fluid, and electrochemical interactions to allow continuous physiological condition and disease monitoring for users. Among them, tear fluid has been widely utilized in the investigation of ocular diseases, diabetes, and even cancers, because of its easy accessibility, lower complexity, and minimal invasiveness. By determining the concentration change of analytes within the tear fluid, it would be possible to identify disease progression and allow patient-oriented therapies. Considering the emerging trend of tear-based biosensing technology, this review article aims to focus on an overview of the tear fluid as a detection medium for certain diseases, such as ocular disorders, diabetes, and cancer. In addition, the rise and application of minimally invasive detection and monitoring via integrated contact lens biosensors will also be addressed, in regards to their practicality and current developmental progress.

New approach to monitoring hull condition of ships as objective for selecting optimal docking period

ABSTRACTThis paper examines methods of ship’s hull and propeller condition monitoring as aim to select optimum docking period. So far present methods did not consider continuous condition monitoring of hull and propeller as a means of determining docking period. This paper proposes optimisation of docking period based on data collecting and data processing. Proposed approach to data analysis as well as calculation of energy efficiencies losses, caused by hull and propeller fouling, can be used as an acceptable method for shipping companies to make decision on the most optimal docking period. This method is aligned with ISO 19030 standard, particularly with part 3 ‘alternative methods’ which specify data collection on daily basis without the use of complex computing equipment. The method, proposed in this paper, is particularly acceptable to companies that do not have the opportunity to invest in sophisticated data collection and processing equipment.

MACHINE HEALTH MONITORING USING VIBRATION ANALYSIS

ABSTRACTVibration analysis is an effective tool to identify and predict failure using SignalProcessing based on condition monitoring. Accordingly, machine faults can bedetected and diagnosed using different post processing of vibration signature. Thepresent study is to prove that Machine Health and availability achievement becomingeasier by using different vibration analysis techniques. Three different types ofnonstationary signals are addressed in this paper to evaluate the technical conditionsof Paper Machine Forming Roll. Forming Roll consists of frequency drive motor,gearbox and a symmetric rotor supported on two similar bearings. The present studyis focusing on Forming Roll Bearings Condition Monitoring. A cost wise comparisonof maintenance strategies is made to confirm that Condition based Maintenance isthe most cost-effective strategy. In the past portable data-collectors were used tomeasure vibration levels, but Condition based Monitoring had taken place to avoidany sudden fault developing before next time schedule. Applying different vibrationanalysis techniques such as Spectrum, Waveform and Impact Demodulation willsupport fault detection, diagnosis and decision-making. The present study provedthat Impact demodulation is recommended to specify faulty bearing source locationand severity, while time waveform or spectrum analysis techniques can detect faultprogress but couldn’t distinguish severity due to bearings similarity. Based on ImpactDemodulation technique decision is made to exchange only the higher severity faultybearing and postponed the other one for next planned shutdown. Consequently, themachine availability and health can be achieved by reducing vibration impact around100 % through continuous condition monitoring.

A Markovian model for power transformer maintenance

The condition of the insulation paper is one of the key determinants of the lifetime of a power transformer. The winding insulation paper may deteriorate aggressively and result in the unexpected failure of power transformers, especially under the presence of high moisture, oxygen, and metal contaminants. Such types of scenarios can be prevented if the deterioration is detected on time. Various types of condition monitoring techniques have been developed to detect transformer condition such as dissolved gas analysis (DGA) and frequency response analysis (FRA). They are non-intrusive and provide early warning of accelerated deterioration both chemically and mechanically. However, the accuracy of those techniques is imperfect, which means periodic inspection is still indispensable. In this paper, we discuss the value of continuous condition monitoring for power transformers and present a way to estimate this value. Towards this, a continuous-time Markov decision model is presented to optimize periodic inspections, so that the cost is minimized and the availability is maximized. We then analyze the performance based on the information from both discrete inspection and continuous condition monitoring using DGA and FRA. The result shows the dissolved gas analysis can improve the availability and operation cost, while frequency response analysis can only improve the availability of power transformers.

Принятие решений о состоянии изоляции печного трансформатора с использованием обобщенного показателя на основе логики с нечеткими множествами

Powerful transformers at PJSC Magnitogorsk Iron and Steel Works are equipped with continuous condition monitoring systems. Available diagnostic system uses the electric method to measure an apparent discharge and a partial discharge intensity (PDI).There is a challenge to develop a decision-making system that could detect an insulation defect and estimate its severity for further transformer operation. We propose to reduce the total of diagnostic variables to a common generalized deterministic parameter reflecting numerical and quality status of the insulation. For this purpose, we use the mathematical apparatus built upon composition of fuzzy linguistic variables and metric values of two measurable parameters of the diagnosable furnace transformer. Reliability and convenience of the proposed method are proven by corresponding calculation results based on data supplied by the industrial on-line monitoring system. These results demonstrate an adequate model behavior with regard to furnace transformers.

Opportunities to use satellite technologies for asset condition monitoring of power networks under the European Space Agency's Integrated Applications Promotion programme

Network operators in the UK have stated their intention to target network reliability through improved procedures for asset management. This involves having a better understanding of how an asset deteriorates over time and their current health. The availability of good quality data is essential to this process. At the distribution level the majority of asset condition data comes from inspection, sometimes coupled with a form of continuous condition monitoring. Both are relatively expensive, with inspection also being very personnel-intensive. Satellites provide a variety of enabling capabilities that are applicable across many sectors. They can potentially support the inspection process of many of the assets present within distribution and transmission networks. This study presents an overview of potential satellite applications within the power industry, with particular emphasis on how they can be used for asset inspection and monitoring. A workshop was organised by European Space Agency and the Power Networks Demonstration Centre with UK network operators as participants to further explore some of the potential applications. The main areas identified are briefly outlined in this study.

Condition-based release of maintenance jobs in a decentralised production-maintenance system – An analysis of alternative stochastic approaches

A condition-based release of preventive maintenance jobs is analysed for a single-stage production/maintenance system where maintenance is performed by a separate unit. It is assumed, that the conditions of the machine deteriorate as a function of multiple production parameters. The task of maintenance is to keep up a predefined operational reliability. In this context the problem of determining the optimal machine condition that triggers the release of a preventive maintenance job arises. Existing approaches to solve this problem either assume a monolithic production/maintenance system or focus on a decentralised system in which information flow and resource transfer do not cause delays. In our paper we derive a triggering mechanism that is not based on these assumptions. It is built on a specific continuous condition monitoring and a suitable information exchange. On this basis the factors determining the delay between the release and completion of a maintenance job are operationalised and alternative stochastic approaches to calculate the triggering condition are formulated. Finally, based on simulations the performance of the approaches is analysed. Thereby, relevant characteristics of the production/maintenance system and the maintenance task are varied systematically. This research contributes answers to the questions on how the exchange of local information can be structured, and the parameters of condition-based maintenance can be calculated and set in case of incomplete information about deterioration in a decentralised production/maintenance system.

Flexible Organic Amplifiers

Signal processing is fundamental to the modern information society. Sensing and signal processing of various biological signals using human body conformable flexible organic integrated circuits have been actively researched to enable continuous health condition monitoring via wearable devices. To amplify very small electrical biological signals from humans, high performance flexible organic amplifiers are required. In this paper, we briefly review the principles and recent progresses of printable organic amplifiers, including materials, processes, and circuits.

Multi-class US traffic signs 3D recognition and localization via image-based point cloud model using color candidate extraction and texture-based recognition

Continuous condition monitoring and inspection of traffic signs are essential to ensure that safety and performance criteria are met. The use of 3D point cloud modeling by the construction industry has been significantly increased in recent years especially for recording the as-is conditions of facilities. The high-precision and dense 3D point clouds generated by photogrammetry can facilitate the process of asset condition assessment. This paper presents an automated computer-vision based method that detects, classifies, and localizes traffic signs via street-level image-based 3D point cloud models. The proposed pipeline integrates 3D object detection algorithm. An improved Structure-from-Motion (SfM) procedure is developed to create a 3D point cloud of roadway assets from the street level imagery. In order to assist with accurate 3D recognition and localization by color and texture features extraction, an automated process of point cloud cleaning and noise removal is proposed. Using camera pose information from SfM, the points within the bounding box of detected traffic signs are then projected into the cleaned point cloud by using the triangulation method (linear and non-linear) and the 3D points corresponding to the traffic sign in question are labeled and visualized in 3D. The proposed framework is validated using real-life data, which represent the most common types of traffic signs. The robustness of the proposed pipeline is evaluated by analyzing the accuracy in detection of traffic signs as well as the accuracy in localization in 3D point cloud model. The results promise to better and more accurate visualize the location of the traffic signs with respect to other roadway assets in 3D environment.