Underwatch has successfully monitored key strategic/critical infrastructural nodes around the world. It has been invaluable for Big Data Analytics consultative services, via vulnerability analyses, risk assessments, and resiliency assessments, such as at electrical grid terminal substations that supply power to these key nodes, which include airfields and international airports.
The referenced analyses/assessments, which are construed to be Big Data Analytics consultative services in the field of strategic/critical infrastructural maintenance, have illuminated blindspots and yielded significant insights, such as ad hoc paradigms, which should not exist.
As an example, the positioning of a normally grounded, isolated indoor paradigm of key distribution feeders to temporary outdoor yard positions constitutes a non-ideal situation; in fact, there is the distinct possibility that the excessive ambient temperature conditions (i.e. non-climate-controlled outdoor environment) of the distribution substation’s yard, where the temporary outdoor feeder resides, can cause the temperature within a recloser control cabinet to dramatically increase. The ensuing thermal stress can affect the insulation of both the enclosed equipment and the involved cables, thereby potentially leading to a possible explosion of the involved recloser control cabinet at the substation.
In essence, an ad hoc paradigm can lead to a temporary outdoor installation, which can segue to thermal stress that can accelerate the aging mechanics of the involve components, thereby causing equipment failure and a possible explosion.
Robust infrastructural maintenance, via Big Data Analytics consultative services, is a crucial factor in preventing failures, such as the aforementioned, from occurring. Infrastructural maintenance strategies are typically divided into three main classes: corrective maintenance, preventive maintenance, and predictive maintenance.
Corrective Maintenance: a common approach to performing the requisite infrastructural maintenance. Corrective maintenance allows the system to operate until the equipment fails, and the equipment is replaced after the failure occurs. For a mission-critical operation, this maintenance strategy is not optimal.
Preventive Maintenance: a time–based approach to performing the requisite infrastructural maintenance. This strategy centers upon performing the service or overhaul after a particular period of time. However, this maintenance strategy has an inherent deficiency; by way of example, if the substation performs maintenance or overhauls biannually, there will be a six-month gap in time between each maintenance cycle. Hence, early warning signs (particularly those that cannot be detected by eyesight), such as heat spots, abnormal temperature rises, partial discharges, etc., that could lead to catastrophic failure of the involved equipment, are often missed.
Predictive Maintenance: a forward-leaning approach based upon the concept of avoiding a failure by estimating the potential of failure and analyzing the resiliency of the involved electrical equipment. Predictive maintenance is also referred to as Condition-Based Maintenance (CBM), Risk-Based Maintenance (RBM), and Performance-Based Maintenance (PBM). The mutual principle and aim of predictive maintenance is to estimate (in many cases, the resolution is too low to yield anything detectable) the condition of the equipment. An online monitoring system has become a well-known approach for evaluating the condition of each individual device within the system. Generally, the online condition monitoring system requires sensors to be installed at the field device so as to collect the data and send it back to an operations center for analysis and display. The data from the online monitoring system together with other factors (such as the risk assessment and replacement cost) will be used to estimate the appropriate time for maintenance or overhaul.
To address the shortfall of current preventive maintenance and predictive maintenance approaches, Underwatch strives to robustly detect early warning signs, via a myriad of parameters, and robustly estimate, via high-resolution telemetry data, the actual condition of the involved equipment. Underwatch utilizes an Artificial Intelligence (AI) platform, which includes state-of-the art aberration detection methodologies. Underwatch can “see the forest from the trees” for Big Data Analytics decision support. It hybridizes Convolutional Adversarial Neural Networks (to see the trees) with Support Vector Machines (to see the forest) and well as other AI methods. The methodological engine utilized by Underwatch has been peer reviewed and successfully fielded at various electrical grid terminal substations that supply load to various airfields and international airports as well as hospitals and healthcare facilities that serve the local community-at-large.
In addition to Energy Resiliency Assessments, other specialized consultative assessment competencies include Cyber Electromagnetic Spectrum Assessments. To date, the resultant insights have provided critical context regarding key critical infrastructural nodes affecting the airfield/airport and/or hospital/health care facility being assessed. Overwatch has been of high value-added proposition and has been considered to be invaluable for business consulting in the field of Big Data Analytics for the purposes of mitigating vulnerabilities, reducing risk, and enhancing resiliency.
Collectively, the insights derived from Underwatch, during the process of business consulting in the field of Big Data Analytics, facilitates the mitigation (e.g. maintenance, repair) process as pertains to the involved components, which include, but are not limited to, computer networking hardware.
Overall, Underwatch has been successfully utilized for infrastructural predictive maintenance with condition monitoring. Taking the prior example of the recloser control unit, which may operate under extremely high ambient temperatures, a temperature sensor (among other sensors) can be very useful for monitoring the condition of the device. Thus, if the temperature exceeds a certain criteria (which could induce thermal stress and insulation degradation), an aberration is logged. Additional sensors, such as noise detection, gas detection, current and voltage channels, as well as sensors at the disconnecting switch, lightning arrestor, and distribution transformer can all provide critical telemetry data to be fused, analyzed, and visualized by Underwatch.
Temperature, Noise Detection, Gases Detection, as well as Current and Voltage Sensors...
Sensors at the disconnecting switch, lightning arrestor, and distribution transformer...
Underwatch underpins Big Data Analytics consultative services related to mission-critical systems (e.g. strategic/critical infrastructural systems including Supervisory Control and Data Acquisition System-related and Disturbance Monitoring Equipment-related components) condition monitoring, which can lead to blindspot identification and anomalous pattern detection amidst large datasets within the Big Data domain.
In this way, Underwatch can help prevent technological disasters. Among the many lessons learned from the National Aeronautics and Space Administration (NASA) Space Shuttle Challenger (Orbiter Vehicle or OV-99) explosion in 1986, all it takes is one component — such as the “O-Ring” (the primary and secondary O-rings, which were designed to prevent a leakage of hot gases were incapable of properly sealing the gaps between the Solid Rocket Booster joints in extremely cold weather) — to cause catastrophic failure of the system as a whole.
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