On-line transformer condition monitoring through diagnostics and anomaly detection

This paper describes the end-to-end components of an on-line system for diagnostics and anomaly detection. The system provides condition monitoring capabilities for two in- service transmission transformers in the UK. These transformers are nearing the end of their design life, and it is hoped that intensive monitoring will enable them to stay in service for longer. The paper discusses the requirements on a system for interpreting data from the sensors installed on site, as well as describing the operation of specific diagnostic and anomaly detection techniques employed. The system is deployed on a substation computer, collecting and interpreting site data on-line

The role of circumstance monitoring on the diagnostic interpretation of condition monitoring data

Circumstance monitoring, a recently coined termed defines the collection of data reflecting the real network working environment of in-service equipment. This ideally complete data set should reflect the elements of the electrical, mechanical, thermal, chemical and environmental stress factors present on the network. This must be distinguished from condition monitoring, which is the collection of data reflecting the status of in-service equipment. This contribution investigates the significance of considering circumstance monitoring on diagnostic interpretation of condition monitoring data. Electrical treeing partial discharge activity from various harmonic polluted waveforms have been recorded and subjected to a series of machine learning techniques. The outcome provides a platform for improved interpretation of the harmonic influenced partial discharge patterns. The main conclusion of this exercise suggests that any diagnostic interpretation is dependent on the immunity of condition monitoring measurements to the stress factors influencing the operational conditions. This enables the asset manager to have an improved holistic view of an asset's health

Interpretation of partial discharge activity in the presence of harmonics

Recent work has identified that circumstances of equipment operation can radically change condition monitoring data. This contribution investigates the significance of considering circumstance monitoring on the diagnostic interpretation of such condition monitoring data. Electrical treeing partial discharge data have been subjected to a data mining investigation, providing a platform for classification of harmonic influenced partial discharge patterns. The Total Harmonic Distortion (THD) index was varied to a maximum of 40%. The results show progressive development for interpretation of condition monitoring data, improving the asset manager's holistic view of an asset's health

Assessing the effects of power quality on partial discharge behaviour through machine learning

Partial discharge (PD) is commonly used as an indicator of insulation health in high voltage equipment, but research has indicated that power quality, particularly harmonics, can strongly influence the discharge behaviour and the corresponding pattern observed. Unacknowledged variation in harmonics of the excitation voltage waveform can influence the insulation's degradation, leading to possible misinterpretation of diagnostic data and erroneous estimates of the insulation's ageing state, thus resulting in inappropriate asset management decisions. This paper reports on a suite of classifiers for identifying pertinent harmonic attributes from PD data, and presents results of techniques for improving their accuracy. Aspects of PD field monitoring are used to design a practical system for on-line monitoring of voltage harmonics. This system yields a report on the harmonics experienced during the monitoring period

Tissue accumulation of cephalothin in burns: A comparative study by microdialysis of subcutaneous interstitial fluid cephalothin concentrations in burn patients and healthy volunteers

Burn tissue sites are a potential source of bacteremia during debridement surgery. Burn injury is likely to affect the distribution of antibiotics to tissues, but direct evidence of this is lacking. The aim of this study was to directly evaluate the influence of burn trauma on the distribution of cephalothin to peripheral tissues. We used subcutaneous microdialysis techniques to monitor interstitial fluid concentrations of cephalothin in the burnt and nonburnt tissues of adult patients with severe burns following parenteral administration of 1 g cephalothin for surgical prophylaxis. Analogous simultaneous studies conducted with healthy adult volunteers provided reference tissue concentration data. Equivalent tissue exposures were seen for burn and nonburn sites, giving overall median interstitial cephalothin concentrations (from 0 to 240 min) of 2.84 mg/liter and 3.06 mg/liter, respectively. A lower overall median interstitial cephalothin concentration of 0.54 mg/liter was observed for healthy individuals, and the patient nonburnt tissue and volunteer control tissue cephalothin concentrations exhibited significantly different data distributions (P < 0.001; Kolmogorov-Smirnov nonparametric test). The duration of tissue residence for cephalothin was longer for burn patients than for healthy volunteers. The results demonstrate the potential fallibility of using healthy population models to extrapolate tissue pharmacodynamic predictions from plasma data for burn patients

Improved non-invasive positron emission tomographic imaging of chemotherapy-induced tumor cell death using Zirconium-89-labeled APOMAB®

Purpose: The chimeric monoclonal antibody (mAb) chDAB4 (APOMAB®) targets the Lupus associated (La)/Sjögren Syndrome-B (SSB) antigen, which is over-expressed in tumors but only becomes available for antibody binding in dead tumor cells. Hence, chDAB4 may be used as a novel theranostic tool to distinguish between responders and nonresponders early after chemotherapy. Here, we aimed to ascertain which positron emitter, Zirconium-89 ([⁸⁹Zr]Zr(IV)) or Iodine-124 ([¹²⁴I]I), was best suited to label chDAB4 for post-chemotherapy PET imaging of tumor-bearing mice and to determine which of two different bifunctional chelators provided optimal tumor imaging by PET using [⁸⁹Zr]Zr(IV)-labeled chDAB4. Methods: C57BL/6 J mice bearing subcutaneous syngeneic tumors of EL4 lymphoma were either untreated or given chemotherapy, then administered radiolabeled chDAB4 after 24 h with its biodistribution examined using PET and organ assay. We compared chDAB4 radiolabeled with [⁸⁹Zr] Zr(IV) or [¹²⁴I] I, or [⁸⁹Zr]Zr-chDAB4 using either DFO-NCS or DFOSq as a chelator. Results: After chemotherapy, [⁸⁹Zr]Zr-chDAB4 showed higher and prolonged mean (± SD) tumor uptake of 29.5 ± 5.9 compared to 7.8 ± 1.2 for [¹²⁴I] I -chDAB4. In contrast, antibody uptake in healthy tissues was not affected. Compared to DFO-NCS, DFOSq did not result in significant differences in tumor uptake of [⁸⁹Zr]Zr-chDAB4 but did alter the tumor:liver ratio in treated mice 3 days after injection in favour of DFOSq (8.0 ± 1.1) compared to DFO-NCS (4.2 ± 0.7). Conclusion: ImmunoPET using chDAB4 radiolabeled with residualizing [⁸⁹Zr] Zr(IV) rather than [¹²⁴I] I optimized post-chemotherapy tumor uptake. Further, PET imaging characteristics were improved by DFOSq rather than DFO-NCS. Therefore, the radionuclide/chelator combination of [⁸⁹Zr] Zr(IV) and DFOSq is preferred for the imminent clinical evaluation of chDAB4 as a selective tumor cell death radioligand.Vasilios Liapis, William Tieu, Stacey E. Rudd, Paul S. Donnelly, Nicole L. Wittwer, Michael P. Brown, and Alexander H. Staudache

Positron emission tomographic imaging of tumor cell death using zirconium-89-labeled APOMAB(R) following cisplatin chemotherapy in lung and ovarian cancer xenograft models

Published online 06 July 2021Purpose Early detection of tumor treatment responses represents an unmet clinical need with no approved noninvasive methods. DAB4, or its chimeric derivative, chDAB4 (APOMAB®) is an antibody that targets the Lupus associated antigen (La/SSB). La/SSB is over-expressed in malignancy and selectively targeted by chDAB4 in cancer cells dying from DNA-damaging treatment. Therefore, chDAB4 is a unique diagnostic tool that detects dead cancer cells and thus could distinguish between treatment responsive and nonresponsive patients. Procedures In clinically relevant tumor models, mice bearing subcutaneous xenografts of human ovarian or lung cancer cell lines or intraperitoneal ovarian cancer xenografts were untreated or given chemotherapy followed 24h later by chDAB4 radiolabeled with [⁸⁹Zr]ZrIV. Tumor responses were monitored using bioluminescence imaging and caliper measurements. [⁸⁹Zr]Zr-chDAB4 uptake in tumor and normal tissues was measured using an Albira SI Positron-Emission Tomography (PET) imager and its biodistribution was measured using a Hidex gamma-counter. Results Tumor uptake of [⁸⁹Zr]Zr-chDAB4 was detected in untreated mice, and uptake significantly increased in both human lung and ovarian tumors after chemotherapy, but not in normal tissues. Conclusion Given that tumors, rather than normal tissues, were targeted after chemotherapy, these results support the clinical development of chDAB4 as a radiodiagnostic imaging agent and as a potential predictive marker of treatment response.Vasilios Liapis, William Tieu, Nicole L. Wittwer, Tessa Gargett, Andreas Evdokiou, Prab Takhar, Stacey E. Rudd, Paul S. Donnelly, Michael P. Brown, Alexander H. Staudache

Toward sustainable environmental quality : priority research questions for Europe

The United Nations' Sustainable Development Goals have been established to end poverty, protect the planet, and ensure prosperity for all. Delivery of the Sustainable Development Goals will require a healthy and productive environment. An understanding of the impacts of chemicals which can negatively impact environmental health is therefore essential to the delivery of the Sustainable Development Goals. However, current research on and regulation of chemicals in the environment tend to take a simplistic view and do not account for the complexity of the real world, which inhibits the way we manage chemicals. There is therefore an urgent need for a step change in the way we study and communicate the impacts and control of chemicals in the natural environment. To do this requires the major research questions to be identified so that resources are focused on questions that really matter. We present the findings of a horizon-scanning exercise to identify research priorities of the European environmental science community around chemicals in the environment. Using the key questions approach, we identified 22 questions of priority. These questions covered overarching questions about which chemicals we should be most concerned about and where, impacts of global megatrends, protection goals, and sustainability of chemicals; the development and parameterization of assessment and management frameworks; and mechanisms to maximize the impact of the research. The research questions identified provide a first-step in the path forward for the research, regulatory, and business communities to better assess and manage chemicals in the natural environment. Environ Toxicol Chem 2018;9999:1-15

An incremental knowledge based approach to the analysis of partial discharge data

Defects in transformer insulation cause partial discharges (PD) which over time can progressively deteriorate the insulating material and possibly lead to electrical breakdown. Therefore, the early detection of the PD is crucial. A PD emits an electromagnetic signal in the ultra high frequency range, and current digital hardware has made it possible to transform this raw data into Phase-Resolved Partial Discharge (PRPD) pattern. Automated PD diagnositc systems previously employed pattern recognition techniques. However, specialists are now able to identify features of the PRPD pattern and deduce different behaviours and therefore physical geometical aspects of the defect. Using this knowledge within a knowledge-based system provides and explanation, and therefore reassurance of the diagnosed fault. This paper describes how to capture and model the knowledge from experts, along with the construction of a rule-based system. It presents a case study of the system's use and the introduction of explanation when diagnosing a defect. The next stage of the monitoring process involves linking the online PD data capture to further diagnostic algorithms and user interfaces. This paper illustrates how this knowledge-based system integrates into an overall transformer monitoring system to provide further data handling and interpretation

An incremental knowledge based approach to the analysis of partial discharge data

Defects in transformer insulation cause partial discharges (PD) which over time can progressively deteriorate the insulating material and possibly lead to electrical breakdown. Therefore, the early detection of the PD is crucial. A PD emits an electromagnetic signal in the ultra high frequency range, and current digital hardware has made it possible to transform this raw data into Phase-Resolved Partial Discharge (PRPD) pattern. Automated PD diagnositc systems previously employed pattern recognition techniques. However, specialists are now able to identify features of the PRPD pattern and deduce different behaviours and therefore physical geometical aspects of the defect. Using this knowledge within a knowledge-based system provides and explanation, and therefore reassurance of the diagnosed fault. This paper describes how to capture and model the knowledge from experts, along with the construction of a rule-based system. It presents a case study of the system's use and the introduction of explanation when diagnosing a defect. The next stage of the monitoring process involves linking the online PD data capture to further diagnostic algorithms and user interfaces. This paper illustrates how this knowledge-based system integrates into an overall transformer monitoring system to provide further data handling and interpretation