六轴协作机械臂异常声压预测数据

通过分析机械臂关节噪声的频率、传播距离、振动幅值、介质密度及声速等核心参数，可准确计算其运行时的声压特征值。结合关节噪声的安全阈值范围，能够快速判断是否存在异常（如谐波减速器磨损、伺服电机故障或机械卡滞）。该监测方法可实现早期故障预警，大幅降低产线停机风险，同时为机械臂的预防性维护提供科学依据，确保工业自动化生产的安全性与连续性。1.数据搜集：搜集到六轴协作机械臂运行的声学振动信号数据，并将其作为特征变量。同时，收集对应的声压数据，作为目标变量。2.预处理：利用归一化公式x=（xi-min）/（max-min）；其中xi是样本字段中第i条数据，x是归一化后的值；3.模型训练：用Python语言环境运行，方法为XGBoost算法，预测六轴协作机械臂的声压。XGBoost的目标函数由两部分组成：损失函数和正则化项。损失函数用于衡量模型预测值与实际值之间的差异，而正则化项则用于控制模型的复杂度，防止过拟合。公式如下：F(x）=求和L（yi，y^i）+求和（m(f(k))），其中L（yi，y^i）表示第i个样本的损失函数，yi是实际值，y^i是预测值；m(f(k))表示第 k 棵树的复杂度。当损失函数F（x）最小化时则停止迭代过程，在此过程中y^i预测值是通过特征函数进行求解的，具体计算方式如下：通过对于目标函数的求导，紧接着并令导数等于0，从而解出叶子节点的最优分数。通过不断地选择最优分裂点并构建树结构，XGBoost最终可以得到一棵最优的树模型。然后，将多棵树模型的预测结果进行累加，即可得到最终的预测值。4.模型迭代和更新，利用交叉验证来评估模型的稳定性和性能，其中交叉验证公式如下：m=求和（yi-y^i）/N；其中m代表交叉验证误差、N代表数据样本的数量、yi代表第 i 个样本的实际观测值、y^i代表第 i 个样本的模型预测值。若满足交叉验证的误差范围则退出迭代过程，输出模型参数值。5.声压（dB）异常判定规则：​如果-20 dB ≤ 声压 ≤ 10 dB，则说明声压稳定；如果声压 > 10 dB或者< -20 dB，则说明声压异常。

By analyzing core parameters of robotic arm joint noise including frequency, propagation distance, vibration amplitude, medium density and sound velocity, the sound pressure characteristic values during operation can be accurately calculated. Combined with the safety threshold range of joint noise, abnormalities such as harmonic reducer wear, servo motor failure or mechanical jamming can be quickly identified. This monitoring method enables early fault warning, greatly reduces the risk of production line downtime, and provides a scientific basis for preventive maintenance of robotic arms, ensuring the safety and continuity of industrial automated production.
1. Data Collection: Acoustic vibration signal data from the operation of 6-axis collaborative robotic arms are collected as feature variables, and corresponding sound pressure data are collected as target variables.
2. Preprocessing: The normalization formula is defined as $x = (x_i - ext{min}) / ( ext{max} - ext{min})$, where $x_i$ denotes the i-th data point in the sample set, and $x$ represents the normalized value.
3. Model Training: The experiment is implemented in the Python environment, using the XGBoost algorithm to predict the sound pressure of 6-axis collaborative robotic arms. The objective function of XGBoost consists of two parts: the loss function and the regularization term. The loss function measures the difference between the model's predicted values and the actual values, while the regularization term controls the model's complexity to prevent overfitting. The formula is as follows: $F(x) = sum L(y_i, hat{y}_i) + sum m(f^{(k)})$, where $L(y_i, hat{y}_i)$ represents the loss function of the i-th sample, $y_i$ is the actual value, $hat{y}_i$ is the predicted value, and $m(f^{(k)})$ denotes the complexity of the k-th decision tree. The iteration stops when the objective function $F(x)$ is minimized. During this process, the predicted value $hat{y}_i$ is solved via the feature function: the objective function is differentiated, and the derivative is set to 0 to obtain the optimal score of the leaf nodes. By continuously selecting optimal split points and constructing tree structures, XGBoost finally obtains an optimal tree model. The final predicted value is obtained by accumulating the prediction results of multiple tree models.
4. Model Iteration and Update: Cross-validation is used to evaluate the stability and performance of the model. The cross-validation error formula is as follows: $m = sum (y_i - hat{y}_i) / N$, where $m$ represents the cross-validation error, $N$ denotes the number of data samples, $y_i$ is the actual observed value of the i-th sample, and $hat{y}_i$ is the model's predicted value of the i-th sample. The iteration exits and the model parameters are output when the cross-validation error falls within the allowable range.
5. Sound Pressure (dB) Abnormality Judgment Rules: If $-20 , ext{dB} leq ext{sound pressure} leq 10 , ext{dB}$, the sound pressure is considered stable; if the sound pressure is greater than 10 dB or less than -20 dB, the sound pressure is deemed abnormal.