This study investigates the cyclic dynamics of cumulative heat release data past the edge of stability in a dilute spark-ignition engine. Emphasis is placed on analyzing the cyclic dynamics near the dilute limit where partial burns and misfires are frequent. These events are often followed by a higher-energy cycle due to the feed-forward mechanism present in the residual gases. These patterns are deterministic and increase the coefficient of variation to undesirable levels. Symbol sequence analysis was used to investigate the cyclic dynamics of these low–high patterns. The heat release was partitioned on an energy basis to give physical meaning to each partition and each sequence created when analyzing the symbol sequence results. This partitioning method provided insight into the differences in the dynamics when operating in the misfire or partial burn regime. These differences could impact the control method used.
Cyber-physical systems (CPS) are vulnerable to a variety of cyber, physical, and cyber-physical attacks. The security of a CPS can be enhanced beyond what can be achieved through firewalls and trusted components by building trust from observed and/or expected behavior. These behaviors can be encoded as invariants. Information flows that do not satisfy the invariants are used to identify and isolate a malfunctioning device or cyber intrusion. However, often it is the case that distributed architectures for cyber-physical systems contain multiple access points, which may be physically and/or digitally linked. Thus, invariants may be difficult to determine and/or computationally prohibitive to check in real-time. Researchers have employed a variety of methods to determine these invariants based on analyzing the design of and/or data generated by cyber-physical systems such as water treatment plants, thermal power plants, and electric power grids. This paper compares the effectiveness of detecting attacks on a secure water treatment plant using design-centric invariants versus data-centric rules, the latter generated using a variety of data mining methods. We compare the approaches in terms of maximization of true positives and minimization of false positives.
This study compares the cycle-to-cycle dynamics in heat release between the misfire and partial burn regime in a highly lean-operated spark-ignition engine. The cyclic dynamics in these two regimes are generally deterministic as misfires and partial burns are often followed by a higher-energy cycle due to the feed-forward mechanism present in the residual gases. Both short and long-term patterns are investigated by converting the sequences into association rules. Using association rules, as opposed to symbol sequencing, allowed additional insight into the differences in the dynamics when operating in each regime. The influence of the residual amount and residual temperature on the next-cycle dynamics is also investigated using association rules. Results show there exists similar short-term dynamics between the misfire and partial burn regime. Results also indicate the residual amount impacts the next cycle more than the residual gas temperature. Understanding of the differences in the dynamics between the misfire and partial burn regime and how the residual plays a role could impact the control method used.
<div class="section abstract"><div class="htmlview paragraph">The widespread adoption of boosted, downsized SI engines has brought pre-ignition phenomena into greater focus, as the knock events resulting from pre-ignitions can cause significant hardware damage. Much attention has been given to understanding the causes of pre-ignition and identify lubricant or fuel properties and engine design and calibration considerations that impact its frequency. This helps to shift the pre-ignition limit to higher specific loads and allow further downsizing but does not fundamentally eliminate the problem. Real-time detection and mitigation of pre-ignition would thus be desirable to allow safe engine operation in pre-ignition-prone conditions. This study focuses on advancing the time of detection of pre-ignition in an engine cycle where it occurs. Phase space transforms through time-delay embedding of cylinder pressure and principal component analysis were applied to same-cycle detection of pre-ignition and shown to enable detection on the order of a crank degree earlier than deviation in cylinder pressure can be identified through direct statistical observation of the pressure data. Additionally, it appears that the deviation of the trajectory in phase space may offer the opportunity to extend this method to further extend the detection window and allow more time for mitigation actions to occur.</div></div>
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