Operating in harsh environments, gas turbines may encounter a variety of faults. Failure to promptly detect these faults can severely impact their safe and stable operation. During actual operations, accurately detecting the specific location of faults in gas turbines, such as sensors, actuators, or gas paths, to ensure prompt maintenance and safe, stable operation, is a crucial aspect of gas turbine maintenance. To address this issue, this study proposes a multi-fault detection method based on cross-validation. In situations with limited data, we have designed a classification method grounded in logical reasoning and constructed a cross-validation system. This system will be applied to layered analysis and discrimination of multiple faults, aiming to achieve effective detection of faults in gas turbine sensors, actuators, and air paths. Finally, simulation experiments have verified the effectiveness and feasibility of this method.

On a high-speed link, there may be tens of millions of IP packets per second and millions of active flows. Maintaining the state of each flow is a fundamental task underlying many network functions, such as load balancing and network anomaly detection. There are two important kinds of per-flow states: per-flow size (e.g., the number of packets received by an arbitrary destination IP) and per-flow cardinality (e.g., the number of distinct source IP addresses that contacted each destination IP). In this paper, we focus on the latter kind of states, and define a new problem: online query of per-flow cardinality, in which we query any given flow’s cardinality entirely on the data plane with low time complexity. For this problem, we propose three solutions named On-vHLL, Ton-vHLL and Aton-vHLL, whose time cost are$O(1)$even for the query operation. Our proposed techniques are three folds. First, we redesign the traditional vHLL with new supplementary data structures called incremental update units (IUUs). When a certain flow’s cardinality is queried, these IUUs can avoid scanning the whole data structure and reduce the time complexity to$O(1)$. Second, we apply a HLL register compression technique called TailCut to the On-vHLL sketch, which can save memory cost by 50%. Third, we add a prefilter based on min-heap, alongside the Ton-vHLL sketch. The prefilter is to give each currently sampled top-$k$superspreader a dedicated HyperLogLog estimator for better accuracy. It can also absorb the superspreaders’ packets bypassing the sketch. We evaluate our new sketches by simulation with CAIDA traces. The results show that our On-vHLL, Ton-vHLL and Aton-vHLL sketches need about 5 memory accesses per packet. The time cost of query operation decreases by hundreds of times than the traditional vHLL that can only be queried offline. Meanwhile, the estimation error of flow spread by our Aton-vHLL is comparable to vHLL.