AI在自动驾驶故障诊断与冗余设计中的应用

    [传感器阵列]
    ↓
    [数据预处理] → [实时故障检测]
    ↓            ↓
    [传感器融合] ← [故障反馈]
    ↓
    [决策系统]
    ↓
    [执行机构]

    import tensorflow as tf
    from tensorflow.keras.layers import Input, Dense
    from tensorflow.keras.models import Model
    
    # 构建自动编码器故障检测模型
    def build_anomaly_detector(input_dim, encoding_dim=32):
    input_layer = Input(shape=(input_dim,))
    
    # 编码器
    encoder = Dense(encoding_dim, activation="relu")(input_layer)
    
    # 解码器
    decoder = Dense(input_dim, activation="sigmoid")(encoder)
    
    # 完整自动编码器
    autoencoder = Model(inputs=input_layer, outputs=decoder)
    
    # 仅编码器部分用于特征提取
    encoder_model = Model(inputs=input_layer, outputs=encoder)
    
    autoencoder.compile(optimizer="adam", loss="mse")
    
    return autoencoder, encoder_model
    
    # 示例使用
    # 假设我们有100维的传感器数据
    autoencoder, encoder = build_anomaly_detector(input_dim=100)
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/BbZ1v8Df9rIsnElwgojydaPpTmqN.png)

    import numpy as np
    import gym
    from stable_baselines3 import PPO
    
    class RedundancyManagementEnv(gym.Env):
    def __init__(self, num_components=3):
        super(RedundancyManagementEnv, self).__init__()
        self.num_components = num_components
        self.action_space = gym.spaces.Discrete(num_components)
        self.observation_space = gym.spaces.Box(low=0, high=1, shape=(num_components,))
        
    def reset(self):
        self.state = np.random.random(self.num_components)
        self.primary = 0
        return self._get_obs()
    
    def _get_obs(self):
        return np.array([self.state[i] if i != self.primary else -1 for i in range(self.num_components)])
    
    def step(self, action):
        # 切换到选择的备用组件
        self.primary = action
        
        # 模拟组件状态变化
        self.state += np.random.normal(0, 0.1, self.num_components)
        self.state = np.clip(self.state, 0, 1)
        
        # 计算奖励：主组件可靠性越高，奖励越大
        reward = self.state[self.primary]
        
        # 简单的终止条件
        done = np.any(self.state < 0.1)
        
        return self._get_obs(), reward, done, {}
    
    # 创建和训练强化学习代理
    env = RedundancyManagementEnv()
    model = PPO("MlpPolicy", env, verbose=1)
    model.learn(total_timesteps=10000)
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/mg9Dp6av5zNRtZFbce2rWuyM4Qd0.png)

    pip install tensorflow stable-baselines3 numpy matplotlib
    
    
    bash

    import numpy as np
    import matplotlib.pyplot as plt
    from tensorflow.keras.layers import Input, Dense, LSTM
    from tensorflow.keras.models import Model
    from sklearn.preprocessing import StandardScaler
    
    # 模拟生成多传感器时间序列数据
    def generate_sensor_data(num_samples=1000, num_sensors=5):
    # 基础信号
    t = np.linspace(0, 20*np.pi, num_samples)
    base_signal = np.sin(t)
    
    # 生成各传感器信号（基于基础信号加噪声）
    sensors = []
    for i in range(num_sensors):
        noise = np.random.normal(0, 0.1, num_samples)
        sensors.append(base_signal + noise)
    
    return np.array(sensors).T
    
    # 添加故障到传感器数据
    def add_faults(data, fault_sensors=[], fault_start=500, fault_magnitude=2.0):
    faulty_data = data.copy()
    for sensor in fault_sensors:
        faulty_data[fault_start:, sensor] += fault_magnitude
    return faulty_data
    
    # 构建LSTM自动编码器
    def build_lstm_autoencoder(time_steps, num_features, latent_dim=32):
    inputs = Input(shape=(time_steps, num_features))
    
    # 编码器
    encoded = LSTM(latent_dim, return_sequences=False)(inputs)
    
    # 重复向量
    repeated = tf.keras.layers.RepeatVector(time_steps)(encoded)
    
    # 解码器
    decoded = LSTM(num_features, return_sequences=True)(repeated)
    
    # 完整模型
    autoencoder = Model(inputs, decoded)
    autoencoder.compile(optimizer="adam", loss="mse")
    
    # 编码器模型
    encoder = Model(inputs, encoded)
    
    return autoencoder, encoder
    
    # 主程序
    if __name__ == "__main__":
    # 生成并准备数据
    data = generate_sensor_data()
    scaler = StandardScaler()
    scaled_data = scaler.fit_transform(data)
    
    # 添加故障（传感器1从500开始故障）
    faulty_data = add_faults(scaled_data, fault_sensors=[1])
    
    # 转换为时间序列样本
    time_steps = 10
    X = []
    for i in range(len(faulty_data) - time_steps):
        X.append(faulty_data[i:i+time_steps])
    X = np.array(X)
    
    # 构建并训练模型
    autoencoder, encoder = build_lstm_autoencoder(time_steps, data.shape[1])
    autoencoder.fit(X, X, epochs=50, batch_size=32, validation_split=0.2)
    
    # 检测异常
    reconstructions = autoencoder.predict(X)
    mse = np.mean(np.square(X - reconstructions), axis=(1,2))
    
    # 可视化结果
    plt.figure(figsize=(12, 6))
    plt.plot(mse, label="Reconstruction Error")
    plt.axvline(x=500-time_steps, color="r", linestyle="--", label="Fault Start")
    plt.title("Sensor Fault Detection")
    plt.xlabel("Time Step")
    plt.ylabel("Reconstruction Error (MSE)")
    plt.legend()
    plt.show()
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/ltfNqekY2jrOhCdKWLAwpQxoXP7m.png)

    import numpy as np
    import matplotlib.pyplot as plt
    from enum import Enum
    
    class ComponentState(Enum):
    NORMAL = 0
    DEGRADED = 1
    FAILED = 2
    
    class RedundantSystem:
    def __init__(self, num_components=3):
        self.num_components = num_components
        self.states = [ComponentState.NORMAL] * num_components
        self.active_component = 0
        self.time = 0
        self.failure_rates = [0.01] * num_components  # 正常故障率
        self.degradation_rates = [0.005] * num_components  # 退化率
        
    def update(self):
        self.time += 1
        # 更新组件状态
        for i in range(self.num_components):
            if self.states[i] == ComponentState.NORMAL:
                if np.random.random() < self.failure_rates[i]:
                    self.states[i] = ComponentState.DEGRADED
            elif self.states[i] == ComponentState.DEGRADED:
                if np.random.random() < self.degradation_rates[i]:
                    self.states[i] = ComponentState.FAILED
        
        # 检查当前活动组件状态
        if self.states[self.active_component] == ComponentState.FAILED:
            self.switch_component()
            
    def switch_component(self):
        # 寻找第一个可用的备用组件
        for i in range(self.num_components):
            if i != self.active_component and self.states[i] != ComponentState.FAILED:
                self.active_component = i
                return True
        return False  # 没有可用组件
    
    def get_system_state(self):
        return {
            "time": self.time,
            "active": self.active_component,
            "states": [s.name for s in self.states],
            "system_operational": any(s != ComponentState.FAILED for s in self.states)
        }
    
    # 模拟
    system = RedundantSystem(num_components=3)
    history = []
    
    for _ in range(1000):
    system.update()
    history.append(system.get_system_state())
    if not history[-1]["system_operational"]:
        print(f"System failed at time {system.time}")
        break
    
    # 可视化结果
    plt.figure(figsize=(12, 6))
    for i in range(system.num_components):
    state_series = [1 if h["active"] == i else 0 for h in history]
    plt.plot(state_series, label=f"Component {i} Active")
    
    for i in range(system.num_components):
    failed_times = [j for j, h in enumerate(history) if h["states"][i] == "FAILED"]
    if failed_times:
        plt.axvline(x=failed_times[0], color=f"C{i}", linestyle="--", alpha=0.3, label=f"Component {i} Failed")
    
    plt.title("Redundant System Operation")
    plt.xlabel("Time Step")
    plt.ylabel("Active Component")
    plt.legend()
    plt.show()
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/Padjo38menVZp9w106MOiKAfyvCJ.png)