Stringent confidentiality policies in aerospace domains severely limit accessible spacecraft observation data. However, given the substantial costs of space missions and the proliferation of global satellite constellations, advancing operational maintenance technologies for aerospace equipment is imperative. Consequently, we publicly release the XJTU-SPS dataset to facilitate research in this field.
XJTU-SPS targets spacecraft power system (SPS), one of the most failure-prone and critically consequential subsystems in spacecraft.
As a representative complex industrial system with multi-sensor network, spacecraft power systems exhibit intricate physical couplings and diverse operational regimes. This dataset can be utilized in other domains for validating algorithm generalizability.
Specifically, XJTU-SPS consists of four sub-datasets: (1) XJTU-SPS for Forecasting or Reconstruction (XJTU-SPS for F or R), (2) XJTU-SPS for Work Mode Recognition (XJTU-SPS for MR), (3) XJTU-SPS for Anomaly Detection (XJTU-SPS for AD), and (4) XJTU-SPS for Anomaly Location / Fault Diagnosis (XJTU-SPS for AL or FD).
Meanwhile, XJTU-SPS is supplemented with fully aligned mathematical-physical models enabling simulations and physical knowledge provision, thereby we also anticipate that XJTU-SPS can provide some support to researchers in Physics-Informed Neural Networks (PINNs), digital twins, and predictive control.
Keywords: spacecraft, power system, PINN, digital twin, monitoring and operation maintenance, time series forecasting, time series reconstruction, work mode recognition, anomaly detection, anomaly location, fault diagnosis.
The SPS comprises the following subsystems: solar array (SA), battery sets (BAT), battery charge regulator (BCR), battery discharge regulator (BDR), shunt regulator (SR), power distribution module (PDM), loads (Loads), and others. The SA is the power source of the SPS, responsible for converting solar radiation into electrical energy, and its output power is affected by factors such as solar radiation, temperature, and shadow. The BAT is the power storage unit of the SPS, responsible for storing the electrical energy provided by the SA. The BCR is the charge control unit of the SPS, responsible for controlling the charging of the BAT. The BDR is the discharge control unit of the SPS, responsible for controlling the discharge of the BAT to the Loads. The SR is the energy regulation unit of the SPS, it works with the BCR and BDR to complete the following functions: • When located in the sunlight area and the sunlight is sufficient, the SA charges the BAT and supplies power to the Loads, • When located in the sunlight area and the sunlight is insufficient, the SA and BAT jointly supply power to the Loads, • When located in the shadow area, the SA does not work, and the BAT supplies power to the Loads. The PDM is the power distribution unit of the SPS, responsible for distributing electrical energy to various Loads. The Loads are the power consumption unit of the spacecraft power system, and their power requirements are affected by factors such as the working state of the spacecraft and mission requirements.
`charge' means SA only charges BAT without any load, `shunt' means SA provides energy for both LOAD and BAT, `joint' means SA and BAT provide energy for LOAD simultaneously, `leisure' means there is no load and BAT is not charging, and `discharge' means BAT only supplies energy to LOAD. The spacecraft we simulated is in low Earth orbit (LEO), and its period is 95 minutes. Under normal circumstances, to maximize the energy generated, this spacecraft is in sun-oriented mode. However, when performing tasks such as imaging the ground targets, it is necessary to switch to ground-oriented mode.
We did not measure all battery groups, but only the 2nd, 3rd, and 4th groups of batteries, in fact, the other 2 groups of batteries exhibit similar observational properties.
XJTU-SPS for F or R does not contain any anomalies or faults. This dataset is used for time series forecasting or reconstruction tasks, typically involving spacecraft normal state prediction, energy management, mission planning, etc. The prediction and reconstruction of spacecraft operational states also form the basis for subsequent anomaly detection and fault diagnosis tasks.
XJTU-SPS for F or R consists of 6 subsets, namely: XJTU-SPS for F or R_4cycles, XJTU-SPS for F or R_18cycles, XJTU-SPS for F or R_24cycles, XJTU-SPS for F or R_34cycles, XJTU-SPS for F or R_90cycles, and XJTU-SPS for F or R_94cycles, which simulate 4, 18, 24, 34, 90, and 94 orbital cycles of spacecraft respectively. Users can select, sample, and truncate the data according to their engineering and project needs.
Visualization example of XJTU-SPS for F or R_18cycles:
XJTU-SPS for work Mode Recognition (XJTU-SPS for MR) do not contain any anomalies or faults. This dataset is used for mode recognition, typically involving recognition of spacecraft work condition, task planning, etc. For complex industrial systems with multi-operational condition switching, performing work condition recognition as the initial stage could effectively reduce the false alarm rates in subsequent anomaly detection and the misdiagnosis rates in fault diagnosis.
XJTU-SPS for MR consists of XJTU-SPS for MR_4cycles, XJTU-SPS for MR_18cycles, XJTU-SPS for MR_24cycles, XJTU-SPS for MR_34cycles, XJTU-SPS for MR_90cycles, and XJTU-SPS for MR_94cycles, which simulate 4, 18, 24, 34, 90, and 94 orbital cycles of spacecraft respectively.
Visualization example of XJTU-SPS for MR_4cycles:
XJTU-SPS for Anomaly Detection (XJTU-SPS for AD) is a dataset for spacecraft anomaly detection, which covers a wide range of anomaly situations in spacecraft power systems, effectively evaluating the effectiveness of anomaly detection methods.
XJTU-SPS for AD consists of XJTU-SPS for AD_Train, XJTU-SPS for AD_Test, and XJTU-SPS for AD_Test_AnomalyLabel, which are the training set, test set, and anomaly label file respectively.
Visualization example of XJTU-SPS for AD_Test:
XJTU-SPS for Anomaly Location / Fault Diagnosis (XJTU-SPS for AL or FD) is a dataset for spacecraft anomaly location or fault diagnosis, which covers a wide range of fault situations in spacecraft power systems, effectively evaluating the effectiveness of anomaly location and fault diagnosis methods.
XJTU-SPS for AL or FD consists of normal data files and 17 types of anomaly/fault data files. Each type of anomaly/fault is composed of a training file and a test file.
Its label information is as follows: 0: normal; 1: SA_partial component or branch open circuit; 2: SA_partial component or branch short circuit; 3: SA_component deterioration; 4: BCR_open circuit; 5: BCR_short circuit; 6: BCR_increased power loss; 7: BAT_degradation; 8: BAT_open circuit; 9: Bus_open circuit; 10: Bus_short circuit; 11: Bus_insulation breakdown; 12: PDM1_open circuit or short circuit; 13: PDM2_open circuit or short circuit; 14: PDM3_open circuit or short circuit; 15: Load1_open circuit; 16: Load2_open circuit; 17: Load3_open circuit;
Supplementary Note: The exclusion of BAT short-circuit and Load short-circuit fault types stems from experimental safety constraints. While attempting to simulate BAT short-circuits using an external battery pack alongside onboard batteries, we observed significant safety hazards. Furthermore, observations showed that a sudden short circuit immediately triggers the power system's protection mechanisms, producing experimental signatures indistinguishable from those of an open-circuit fault. Therefore, the simulations of these short-circuit faults can be considered as subsumed under the open-circuit fault category.
Visualization example of SA_partial component or branch short circuit:
XJTU-SPS is accompanied by a corresponding mathematical-physical model that enables simulations fully consistent with the dataset. It supports monitoring and maintenance applications and provides comprehensive physical knowledge for researches on digital twins, PINNs, and related topics.
Visualization example of simulation results for several representative channels are presented. For the comprehensive simulation results of all channels, please refer to this study.
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The dataset is still undergoing final preparation
The data and model will be made available upon acceptance of the paper. We sincerely appreciate your interest and patience!