Context: Various patterns of dynamic routing architectures are used in service- and cloud-based environments, including sidecar-based routing, routing through a central entity such as an event store, or architectures with multiple dynamic routers.
Objective: Choosing the wrong architecture may severely impact the reliability or performance of a software system. This article’s objective is to provide models and empirical evidence to precisely estimate the reliability and performance impacts.
The data set contains electrical and mechanical signals from experiments on three-phase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.
The experimental workbench consists of a three-phase induction motor coupled with a direct-current machine, which works as a generator simulating the load torque, connected by a shaft containing a rotary torque wrench.
- Induction motor: 1hp, 220V/380V, 3.02A/1.75A, 4 poles, 60 Hz, with a nominal torque of 4.1 Nm and a rated speed of 1715 rpm. The rotor is of the squirrel cage type composed of 34 bars.
- Load torque: is adjusted by varying the field winding voltage of direct current generator. A single-phase voltage variator with a filtered full-bridge rectifier is used for the purpose. An induction motor was tested under 12.5, 25, 37.5, 50, 62.5, 75, 87.5 and 100% of full load.
- Broken rotor bar: to simulate the failure on the three-phase induction motor's rotor, it was necessary to drill the rotor. The rupture rotor bars are generally adjacent to the first rotor bar, 4 rotors have been tested, the first with a break bar, the second with two adjacent broken bars, and so on rotor containing four bars adjacent broken.
All signals were sampled at the same time for 18 seconds for each loading condition and ten repetitions were performed from transient to steady state of the induction motor.
- mechanical signals: five axial accelerometers were used simultaneously, with a sensitivity of 10 mV/mm/s, frequency range from 5 to 2000Hz and stainless steel housing, allowing vibration measurements in both drive end (DE) and non-drive end (NDE) sides of the motor, axially or radially, in the horizontal or vertical directions.
- electrical signals: the currents were measured by alternating current probes, which correspond to precision meters, with a capacity of up to 50ARMS, with an output voltage of 10 mV/A, corresponding to the Yokogawa 96033 model. The voltages were measured directly at the induction terminals using voltage points of the oscilloscope and the manufacturer Yokogawa.
Data Set Overview:
- Three-phase Voltage
- Three-phase Current
- Five Vibration Signals
The database was acquired in the Laboratory of Intelligent Automation of Processes and Systems and Laboratory of Intelligent Control of Electrical Machines, School of Engineering of São Carlos of the University of São Paulo (USP), Brazil.
This dataset contains the data associated with three test distribution netwroks with 24, 54, and 118 nodes.
Test networks for reliability-based distribution studies.
This dataset includes 4 test netwroks with 37, 85, 137, and 145 nodes.
Vibration measurement on SAG mill drive motor for Energy harvesting or predictive maintenance
We introduce a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VCSs usage scenarios and using modern voice assistant development kits.
The corpus consists of three sets: the core, evaluation, and complete set. The complete set contains all the data (i.e., complete set = core set + evaluation set) and allows the user to freely split the training/test set. Core/evaluation sets suggest a default training/test split. For each set, all *.wav files are in the /data directory and the meta information is in meta.csv file. The protocol is described in the readme.txt. A PyTorch data loader script is provided as an example of how to use the data. A python resample script is provided for resampling the dataset into the desired sample rate.
This dataset accompanies the article "Palisade: A Framework for Anomaly Detection in Embedded Systems." It contains traces, programs, and specifications used in the case studies from the paper.
Case Study 1: Autonomous Vehicle - Comparison between Siddhi and Palisade nfer processor
- cs1_gear_flip_flop_data.csv - the data used in the Gear Flip-Flop anomaly study and the comparison with Siddhi
- cs1_comparison.nfer - the nfer specification used in the comparison with Siddhi
- cs1_comparison.siddhi - the siddhi specification used in the comparison with Siddhi
Case Study 2: ADAS-on-a-treadmill - Comparison between Beep Beep 3 and Palisade rangeCheck and lossDetect processors
- cs2_platoon_dead_spot_data.csv - the data used in the Platoon Dead-Spot anomaly study and the comparison with Beep Beep 3
- cs2_platoon_no_anomaly_data.csv - data used for training in the Platoon Dead-Spot anomaly study
- cs2_platoon_range_model.json - trained model used by the rangeCheck processor
- RangeCheck.java - Beep Beep 3 program to check both range and loss
- BenchSink.java - Beep Beep 3 program to print events
- BenchPublisher.java - Beep Beep 3 program to read from a file and publish events to the RangeCheck program
- BenchEvent.java - Custom Beep Beep 3 event class used in the comparison
The aircraft fuel distribution system has two primary functions: storing fuel and distributing fuel to the engines. These functions are provided in refuelling and consumption phases, respectively. During refuelling, the fuel is first loaded in the Central Reservation Tank and then distributed to the Front and Rear Tanks. In the consumption phase, the two engines receive an adequate level of fuel from the appropriate tanks. For instance, the Port Engine (PE) will receive fuel from Front Tank and the Starboard Engine (SE) will receive fuel from Rear Tank.
You can easily read the CSV files and apply your method.The dataset has five parts, one normal and four abnormal scenarios.
Data for 24-node and 54-node test networks for reliability-oriented distribution expansion planning applications.