西门子SINAMICS G120驱动通过PROFINET与S7-300/400F Failsafe CPU的故障安全控制应用

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本资源是一份西门子公司提供的技术应用指南，标题为《通过PROFINET通过Failsafe S7-300/400F CPU控制SINAMICS G120驱动器的Failsafe控制方法》。这份文档发布于2014年8月，适用于西门子SINAMICS G120变频器（CU240E-2DP-F型号）与SIMATIC S7-300/400系列PLC（Failsafe版本）之间的集成控制，特别是在PROFINET工业以太网通信网络中的应用。 主要内容涵盖了以下几个关键知识点： 1. **驱动器控制技术**：SINAMICS G120是一款高性能变频器，CU240E-2DP-F型号支持双绞线接口，用于与S7-300/400F PLC通过PROFINET进行通信。这展示了西门子在自动化领域的专业技术，如何利用高级通信协议来确保设备间的可靠连接和数据交换。 2. **Failsafe功能**：文档强调了Failsafe控制的重要性，即在系统故障或紧急情况下保持安全操作的能力。通过S7-300/400F CPU的Failsafe功能，可以实现对变频器的保护性停机和恢复，确保系统的稳定性及人员安全。 3. **TIAPortal的应用**：TIAPortal（ Totally Integrated Automation Portal）是西门子的一种集成软件平台，用户可以通过这个平台实现对变频器的远程监控和控制，包括对安全功能的管理，如急停、故障诊断和维护提示等。 4. **非约束性应用示例**：虽然文档提供了详细的电路示例和安装建议，但需明确，这些示例并非定制解决方案，而是为了展示典型应用场景。用户在实际操作时需自行评估并确保按照安全标准进行设备配置、安装、运行和维护。 5. **责任声明**：西门子公司提醒用户，使用这些应用示例不构成法律义务，用户需自行负责正确使用产品，并承认在应用过程中可能产生的风险，公司仅对根据合同规定承担的责任负责。 这份文档是西门子针对SINAMICS G120变频器与S7-300/400F PLC通过PROFINET实现Failsafe控制的重要参考材料，旨在帮助工程师们理解和优化他们的自动化控制系统，确保设备的安全性和性能。

Infineon-XMC1000-C_Start_and_Device_Initialization-AN-v01_00-EN启动文件说明.pdf

2019-09-01 上传
文档《Infineon-XMC1000-C_Start_and_Device_Initialization-AN-v01_00-EN启动文件说明.pdf》详细介绍了在DAVE开发环境中，如何进行XMC1000系列微控制器的启动文件配置与设备初始化。下面将围绕文档内容，展开对于...

基于s7---300-plc物料分拣系统的设计--大学毕业(论文)设计.doc

2023-07-09 上传
在该系统中，S7-300 PLC Played a crucial role as the controller, responsible for controlling the entire system. Siemens’ programming software STEP 7 was used to design the PLC programs, which enabled ...

/proc/PID/stat

2023-03-29 上传
The /proc/PID/stat file contains various statistics about the process with the specified PID. The file is a single line of text, with each field separated by a space. The fields in the file are as ...

Optimize the following code to use the variable: variance in the code. def e_step(xs: np.ndarray, ys: np.ndarray, affine: np.ndarray, translation: np.ndarray, variance: float) -> np.ndarray: """ The e-step of the em algorithm, estimating the responsibility P=[p(y_m | x_n)] based on current model :param xs: a set of points with size (N, D), N is the number of samples, D is the dimension of points :param ys: a set of points with size (M, D), M is the number of samples, D is the dimension of points :param affine: an affine matrix with size (D, D) :param translation: a translation vector with size (1, D) :param variance: a float controlling the variance of each Gaussian component :return: the responsibility matrix P=[p(y_m | x_n)] with size (N, M), which row is the conditional probability of clusters given the n-th sample x_n """ # TODO: Change the code below and implement the E-step of GMM responsibility = np.ones((xs.shape[0], ys.shape[0])) / ys.shape[0] for n in range(xs.shape[0]): for m in range(ys.shape[0]): temp = -0.5 * np.linalg.norm(xs[n] - ys[m] @ affine - translation) ** 2 responsibility[n, m] = 1 / (2 * np.pi) ** (xs.shape[1] / 2) * np.exp(temp) return responsibility / np.sum(responsibility, axis=1, keepdims=True)

2023-05-29 上传
:param variance: a float controlling the variance of each Gaussian component :return: the responsibility matrix P=[p(y_m | x_n)] with size (N, M), which row is the conditional probability of ...

find bug and fix it: def mean_shift2(xs: np.ndarray, num_iter: int = 50, k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray: """ Implement a variant of mean-shift algorithm, with unchanged kernel matrix :param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features :param num_iter: the number of iterations :param k_type: the type of kernels, including 'rbf', 'gate', 'triangle', 'linear' :param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels :return: the estimated means with size (N, D) """ # TODO: change the code below and implement the modified mean-shift kappa = kernel(xs, y=None, k_type=k_type, bandwidth=bandwidth) D = np.diag(kappa.sum(axis=1)) L = D - kappa D_inv_sqrt = np.diag(1 / np.sqrt(np.diag(D))) L_norm = D_inv_sqrt @ L @ D_inv_sqrt for i in range(xs.shape[0]): x = xs[i] for j in range(num_iter): ms = L_norm @ x x = ms / np.linalg.norm(ms) xs[i] = x return xs

2023-05-29 上传
:param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features :param num_iter: the number of iterations :param k_type: the type of kernels, ...

这段代码社么意思This is a C function named PWM_regulate_1 that appears to be controlling PWM (Pulse Width Modulation) signals on two different channels, PWM_Number_1 and PWM_Number_2. Here's a breakdown of the function: Enable flag_on variable. Disable PWM clock for both channels (PWM_Number_1 and PWM_Number_2). Disable time-base clock synchronization. Enable PWM clock for both channels again. Determine the frequency from data[5] and data[6]. Set flag_freq based on the determined frequency. Calculate the period and dead time based on f and data values. Calculate the duty cycle (D) based on the period and data[7]. Set various configurations for myPwm1 (channel 1): Set the period, phase, and count values. Configure counter mode and disable counter load. Set shadow mode for PWM compare register A. Set high-speed clock division and clock division based on flag_freq. Set the comparison value for PWM compare register A (duty cycle). Configure action qualification for up/down counting mode. Configure dead band output, polarity, and input modes. Set rising and falling edge delay for dead band. Set similar configurations for myPwm2 (channel 2), using the same logic as above. Note: This code snippet seems to be a part of a larger program, missing some essential details such as variable types, initializations, libraries used, and possible hardware connections

2023-07-09 上传
7. 基于f和data值计算周期和死区时间。 8. 根据周期和data[7]计算占空比（D）。 9. 设置myPwm1（通道1）的各种配置： - 设置周期、相位和计数值。 - 配置计数器模式并禁用计数器加载。 - 设置PWM比较寄存器A的...

Continue to refine the following code and don't add any other packages but numpy: def mean_shift2(xs: np.ndarray, num_iter: int = 50, k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray: """ Implement a variant of mean-shift algorithm, with unchanged kernel matrix :param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features :param num_iter: the number of iterations :param k_type: the type of kernels, including 'rbf', 'gate', 'triangle', 'linear' :param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels :return: the estimated means with size (N, D) """ # TODO: change the code below and implement the modified mean-shift return xs

2023-05-29 上传
Here is a possible implementation of the modified mean-shift algorithm: ``` def mean_shift2(xs: np.ndarray, num_iter: int = 50, k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray: ""...

2023-06-09 09:46:11.022252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1900] Ignoring visible gpu device (device: 0, name: GeForce GT 610, pci bus id: 0000:01:00.0, compute capability: 2.1) with Cuda compute capability 2.1. The minimum required Cuda capability is 3.5. 2023-06-09 09:46:11.022646: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_test_function.<locals>.test_function at 0x0000017BB39D0670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_test_function.<locals>.test_function at 0x0000017BB3AE83A0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.

2023-06-10 上传
2. TensorFlow 检测到了一些 CPU 功能，包括 AVX 和 AVX2 指令集。这些指令集可以在一些性能关键的操作中加速计算。为了在其他操作中启用它们，需要使用适当的编译器标志重新构建 TensorFlow。 3. TensorFlow 输出...

Error = InputPoint- pp->rOut; pp->ContrlOut=0.0407*Error-0.04028*pp->error_1 //calculate the controlling quantity directly +0.8187*pp->u_1+(1-0.8187)*pp->u_6; pp->error_2=pp->error_1; pp->error_1=Error; pp->u_6=pp->u_5; /*get the next controlling quantity*/ pp->u_5=pp->u_4; pp->u_4=pp->u_3; pp->u_3=pp->u_2; pp->u_2=pp->u_1; pp->u_1=pp->ContrlOut; if(pp->ContrlOut>=1.0) /*Restricting Maxim PID Out*/ pp->ContrlOut=1.0; if(pp->ContrlOut<0) pp->ContrlOut=0; return pp->ContrlOut; //此公式用到的大林算法离散化方法

2023-05-30 上传
抱歉，我并没有看到这段代码中使用到大林算法的离散化方法。这段代码是一个PID控制器的实现，其中的公式是PID控制器的控制规律。PID控制器的作用是根据当前的误差（偏差）来计算输出量，以实现对被控对象的控制。...

def mean_shift(xs: np.ndarray, num_iter: int = 50, k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray: """ Implement the mean-shift algorithm :param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features :param num_iter: the number of iterations :param k_type: the type of kernels, including 'rbf', 'gate', 'triangle', 'linear' :param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels :return: the estimated means with size (N, D) """ # TODO: change the code below and implement the mean-shift algorithm return

2023-05-29 上传
:param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features :param num_iter: the number of iterations :param k_type: the type of kernels, ...
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