Synthetic Reaction Wheel Datasets for Nominal and Faulty Conditions as Used for Training the One-Dimensional Sliding Window Residual Network Proposed

All of the datasets provided here have been generated in MATLAB. The datasets describe the measurement outputs of the continuous operation of a reaction wheel based on Bialke's high fidelitiy RW model onboard a satellite that undergoes different faults.

If you import any of these datasets into MATLAB, a cell-type variable called "dataset" will show up.

You can also load them for use in Python and other usages.

Datasets #1-#3, #4-#6, and #7-#9 are grouped together and are describing the same signals from different perspectives, therefore,

they have the same number of cells. For example, the 10th cells of datasets #1-#3 describe the same signal.

More information about the datasets is available in Table III in the referenced paper.

For any of the MJAPF and SPF datasets, each cell is of the form 2x1001 doubles.

For these datasets, the first row is the descriptor vector of the signal and the second row corresponds to the signal

as perceived by MJAPF or SPF. Both signals have 1001 time steps. The time steps are 0.1 seconds apart, meaning that 1001 encompasses operational details of the reaction wheel over about 100 seconds.

The raw datasets' cells are of the form 5x1001 doubles.

The first row is, again, the descriptor row.

The second row is the reaction wheel's motor current as received by sensors (with sensor noise).

The third row is the reaction wheel's angular velocity as received by sensors (with sensor noise).

The fourth row is the reaction wheel's motor current with process noise but without the measurement noise (true signals).

The fifth row is the reaction wheel's angular velocity with process noise but without the measurement noise (true signals).

Therefore, the datasets are structured as follows:

1) For MJAPF- or SPF-treated datasets:
First row: Descriptors that describe the health state of the reaction wheel at each time step and have no units since it is a categorical variable.

Second row: The reaction wheel motor's electrical current (I_m), as estimated by MJAPF or SPF, has the unit of Amperes (A).

2) For raw datasets:
First row: Descriptors that describe the health state of the reaction wheel at each time step and have no units since it is a categorical variable.

Second row: The reaction wheel motor measures the electrical current (I_m) without undergoing any treatments and has the unit of Amperes (A).

Third row: The reaction wheel measures angular velocity (omega_m) without undergoing any treatments and has the unit of Radians per second (rad/s).

Fourth row: Reaction wheel motor's real or true process electrical current (I_m) without undergoing any treatments and has the unit of Amperes (A).

Fifth row: Reaction wheel's real or true process angular velocity (omega_m) without undergoing any treatments and has the unit of Radians per second (rad/s).

本数据集均由 MATLAB 生成，详尽描述了基于 Bialke 高保真 RW 模型，在卫星上持续运行的反应轮的测量输出，并涵盖了不同故障情况下的数据。若将此类数据集导入 MATLAB，将出现名为“dataset”的单元类型变量。同时，这些数据集亦适用于 Python 及其他用途。

数据集 #1-#3、#4-#6 以及 #7-#9 归为一组，从不同角度描述相同的信号，因此，它们具有相同数量的单元。例如，数据集 #1-#3 的第 10 个单元描述的是相同的信号。

有关数据集的更多信息，可参考参考文献中的表 III。

对于任何 MJAPF 或 SPF 数据集，每个单元均为 2x1001 个双精度浮点数。对于这些数据集，第一行是信号描述向量，第二行对应于 MJAPF 或 SPF 感知到的信号。两种信号均包含 1001 个时间步长，时间步长间隔为 0.1 秒，即 1001 步涵盖了约 100 秒的反应轮运行细节。

原始数据集的单元形式为 5x1001 个双精度浮点数。第一行是描述行。第二行是传感器接收到的反应轮电机电流（含传感器噪声）。第三行是传感器接收到的反应轮角速度（含传感器噪声）。第四行是未经处理的反应轮电机电流（含过程噪声但无测量噪声，即真实信号）。第五行是未经处理的反应轮角速度（含过程噪声但无测量噪声，即真实信号）。

因此，数据集的结构如下：

1) 对于 MJAPF 或 SPF 处理的数据集：
第一行：描述每个时间步长反应轮健康状况的描述符，由于为分类变量，因此无单位。
第二行：由 MJAPF 或 SPF 估计的反应轮电机电流（I_m），单位为安培（A）。

2) 对于原始数据集：
第一行：描述每个时间步长反应轮健康状况的描述符，由于为分类变量，因此无单位。
第二行：反应轮电机测量的未经处理的电流（I_m），单位为安培（A）。
第三行：未经处理的反应轮测量的角速度（omega_m），单位为每秒弧度（rad/s）。
第四行：未经处理的反应轮电机的真实或真实过程电流（I_m），单位为安培（A）。
第五行：未经处理的反应轮的真实或真实过程角速度（omega_m），单位为每秒弧度（rad/s）。