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This is due to the progression over time of various factors including steadily increasing load demand, engineering forces, and economic factors. The enormous investments in data center and other highly available infrastructure systems perversely incents conditions of elevated risk and higher likelihood of failure. Maximizing capacity, increasing density, and hastening production from installed infrastructure improves the return on investment ROI on these major capital investments.

The increasing density of data center infrastructure exemplifies the dynamics that continually and inexorably push a system towards critical failure. Server density is driven by a mixture of engineering forces advancements in server design and efficiency and economic pressures demand for more processing capacity without increasing facility footprint. Increased density then necessitates corresponding increases in the number of critical heating and cooling elements. Note that engineering improvements and load growth are driven by strong, underlying economic and societal forces that are not easily modified.

Because of this dynamic mix of forces, the potential for a catastrophic outcome is inherent in the very nature of complex systems [Element 6].

Complex Systems Modeling

For large-scale mission critical and business critical systems, the profound implication is that designers, system planners, and operators must acknowledge the potential for failure and build in safeguards. Human error is often cited as the root cause of many engineering system failures, yet it does not often cause a major disaster on its own.


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Based on analysis of 20 years of data center incidents, Uptime Institute holds that human error must signify management failure to drive change and improvement. Although front-line operator error may sometimes appear to cause an incident, a single mistake just like a single data center component failure is not often sufficient to bring down a large and robust complex system unless conditions are such that the system is already teetering on the edge of critical failure and has multiple underlying risk factors.

Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems

For example, media reports after the Exxon Valdez oil spill zeroed in on the fact that the captain, Joseph Hazelwood, was not at the bridge at the time of the accident and accused him of drinking heavily that night. However, more measured assessments of the accident by the NTSB and others found that Exxon had consistently failed to supervise the captain or provide sufficient crew for necessary rest breaks see Figure 2.

Introduction to Complexity: What are Complex Systems?

Figure 2. The picture was taken three days after the vessel grounded, just before a storm arrived. As Dr. Cook points out, post-accident attribution to a root cause is fundamentally wrong [Element 7]. Complete failure requires multiple faults, thus attribution of blame to a single isolated element is myopic and, arguably, scapegoating. Exxon blamed Captain Hazelwood for the accident, and his share of the blame obscures the underlying mismanagement that led to the failure.

Inadequate enforcement by the U.


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Coast Guard and other regulatory agencies further contributed to the disaster. As a result, the rig and its tow vessels undertook a challenging 1,nautical-mile journey across the icy and storm-tossed waters of the Gulf of Alaska in December Funk There had already been a chain of engineering and inspection compromises and shortfalls surrounding the Kulluk, including the installation of used and uncertified tow shackles, a rushed refurbishment of the tow vessel Discovery, and electrical system issues with the other tow vessel, the Aivik, which had not been reported to the Coast Guard as required.

Discovery experienced an exhaust system explosion and other mechanical issues in the following months. Gale-force winds put continual stress on the tow line and winches. The tow ship was captained on this trip by an inexperienced replacement, who seemingly mistook tow line tensile alarms set to go off when tension exceeded tons for another alarm that was known to be falsely annunciating.

At one point the Aivik, in attempting to circle back and attach a new tow line, was swamped by a wave, sending water into the fuel pumps a problem that had previously been identified but not addressed , which caused the engines to begin to fail over the next several hours see Figure 3.


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Waves crash over the mobile offshore drilling unit Kulluk where it sits aground on the southeast side of Sitkalidak Island, Alaska, Jan. A Unified Command, consisting of the Coast Guard, federal, state, local and tribal partners and industry representatives was established in response to the grounding. Despite harrowing conditions, Coast Guard helicopters were eventually able to rescue the 18 crew members aboard the Kulluk.

Valiant last-ditch tow attempts were made by the repaired Aivik and Coast Guard tugboat Alert, before the effort had to be abandoned and the oil rig was pushed aground by winds and currents. Example A Tier III Concurrent Maintenance data center criteria see Uptime Institute Tier Standard: Topology require multiple, diverse independent distribution paths serving all critical equipment to allow maintenance activity without impacting critical load. The data center in this example had been designed appropriately with fuel pumps and engine- generator controls powered from multiple circuit panels.

As built, however, a single panel powered both, whether due to implementation oversight or cost reduction measures. At issue is not the installer, but rather the quality of communications from the implementation team and the operations team. In the course of operations, technicians had to shut off utility power during the performance of routine maintenance to an electrical switchgear. This meant the building was running on engine-generator sets. However, when the engine-generator sets started to surge due to a clogged fuel line. The UPS automatically switched the facility to battery power.

The day tanks for the engine-generator sets were starting to run dry. If quick-thinking operators had not discovered the fuel pump issue in time, there would have been an outage to the entire facility: a cascade of events leading down a rapid pathway from simple routine maintenance activity to complete system failure. Example B Tier IV Fault Tolerant data center criteria require the ability to detect and isolate a fault while maintaining capacity to handle critical load. In this example, a Tier IV enterprise data center shared space with corporate offices in the same building, with a single chilled water plant used to cool both sides of the building.

The office air handling units also brought in outside air to reduce cooling costs. One night, the site experienced particularly cold temperatures and the control system did not switch from outside air to chilled water for office building cooling, which affected data center cooling as well.

Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems

The freeze stat a temperature sensing device that monitors a heat exchanger to prevent its coils from freezing failed to trip; thus the temperature continued to drop and the cooling coil froze and burst, leaking chilled water onto the floor of the data center. There was a limited leak detection system in place and connected, but it had not been fully tested yet. Chilled water continued to leak until pressure dropped and then the chilled water machines started to spin offline in response.

Once the chilled water machines went offline neither the office building nor data center had active cooling. At this point, despite the extreme outside cold, temperatures in the data hall rose through the night. As a result of the elevated indoor temperature conditions, the facility experienced myriad device-level e. In both of these cases, severe disaster was averted, but relying on front-line operators to save the situation is neither robust not reliable.

However, facility infrastructure is only one component of failure prevention; how a facility is run and operated on a day-to-day basis is equally critical. Cook noted, humans have a dual role in complex systems as both the potential producers causes of failure as well as, simultaneously, some of the best defenders against failure [Element 9]. The fingerprints of human error can be seen on the two data center examples.

In Example A, the electrical panel was not set up as originally designed, and the leak detection system, which could have alerted operators to the problem, had not been fully activated in Example B. It was not a lack of standards, but a lack of compliance or sloppiness that contributed the most to the disastrous outcomes.

For example, in the case of the Boeing batteries, the causes were bad design, poor quality inspections, and lack of contractor oversight. If leadership, operators, and oversight agencies had adhered to their own policies and requirements and had not cut corners for economics or expediency, these disasters might have been avoided. Ongoing operating and management practices and adherence to recognized standards and requirements, therefore, must be the focus of long-term risk mitigation.

Figure 1. Common mode failure effects on traditional defences in systems. I also look at how common mode failures are like a microcosm of the kind of complex failures that resilience emerged to deal with. Primary Menu Skip to content. Consequences of the occurrence of faults can be severe and result in human casualties, environmentally harmful emissions, high repair costs, and economical losses caused by unexpected stops in production lines.

The majority of real systems are hybrid dynamic systems HDS. In HDS, the dynamical behaviors evolve continuously with time according to the discrete mode configuration in which the system is. Consequently, fault diagnosis approaches must take into account both discrete and continuous dynamics as well as the interactions between them in order to perform correct fault diagnosis.

This book presents recent and advanced approaches and techniques that address the complex problem of fault diagnosis of hybrid dynamic and complex systems using different model-based and data-driven approaches in different application domains inductor motors, chemical process formed by tanks, reactors and valves, ignition engine, sewer networks, mobile robots, planetary rover prototype etc. Synthesizes the state of the art in the domain of fault diagnosis of hybrid dynamic systems; Studies the complementarities and the links between the different methods and techniques of fault diagnosis of hybrid dynamic systems; Includes the required notions, definitions and background to understand the problem of fault diagnosis of hybrid dynamic systems and how to solve it; Uses multiple examples in order to facilitate the understanding of the presented methods.

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