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| import streamlit as st | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import warnings | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import accuracy_score | |
| warnings.filterwarnings("ignore") | |
| # Load the dataset | |
| df = pd.read_csv(r"C:\Users\91879\Downloads\data.csv") | |
| df.fillna(df.mean(), inplace=True) | |
| # Create Tabs | |
| tab1, tab2, tab3 = st.tabs(["📘 Project Overview", "📊 EDA", "🔍 Fault Prediction"]) | |
| # ----------------------------- TAB 1 --------------------------------- | |
| with tab1: | |
| st.header("🚗 Brake System Fault Detection") | |
| st.markdown("### 🧩 Business Problem") | |
| st.markdown(""" | |
| In the automotive industry, ensuring the safety and reliability of braking systems is **mission-critical**. Traditional brake inspections are typically **manual and reactive**, often identifying problems **only after they occur** or during scheduled maintenance. | |
| However, undetected faults in braking systems can lead to: | |
| - **Brake failure during operation** | |
| - **Reduced vehicle control** | |
| - **Increased risk of accidents** | |
| - **Expensive emergency repairs** | |
| Manufacturers and fleet managers need a **real-time fault detection system** using **sensor data** to: | |
| - Monitor brake system health continuously | |
| - **Predict faults proactively** | |
| - **Minimize vehicle downtime** | |
| - Enhance **safety, reliability, and cost-efficiency** | |
| """) | |
| feature_desc = { | |
| 'Brake_Pressure': "Pressure applied to the brake pedal.", | |
| 'Pad_Wear_Level': "Indicates the wear level of brake pads.", | |
| 'ABS_Status': "1 if Anti-lock Braking System is active, else 0.", | |
| 'Wheel_Speed_FL': "Speed of the front-left wheel.", | |
| 'Wheel_Speed_FR': "Speed of the front-right wheel.", | |
| 'Wheel_Speed_RL': "Speed of the rear-left wheel.", | |
| 'Wheel_Speed_RR': "Speed of the rear-right wheel.", | |
| 'Fluid_Temperature': "Temperature of the brake fluid.", | |
| 'Pedal_Position': "How far the brake pedal is pressed." | |
| } | |
| selected = st.selectbox("Select a feature to understand:", list(feature_desc.keys())) | |
| st.info(f"📘 **{selected}**: {feature_desc[selected]}") | |
| # 🎯 Goal | |
| st.markdown("### 🎯 Goal") | |
| st.markdown(""" | |
| Build a data-driven model that detects braking system faults using sensor data such as brake pressure, wheel speeds, fluid temperature, and pedal position. | |
| """) | |
| # 💼 Business Objective | |
| st.markdown("### 📌 Business Objective") | |
| st.markdown(""" | |
| - Detect faults early to reduce vehicle failure risks. | |
| - Analyze sensor behavior during fault vs non-fault conditions. | |
| - Support preventive maintenance using historical data patterns. | |
| """) | |
| st.markdown("### 📊 Data Understanding") | |
| st.markdown(""" | |
| The dataset contains **real-time sensor readings** collected from a vehicle's braking system to detect faults. | |
| #### 🔢 Numerical Features: | |
| - **Brake_Pressure** | |
| - **Pad_Wear_Level** | |
| - **Wheel_Speed_FL**, **Wheel_Speed_FR**, **Wheel_Speed_RL**, **Wheel_Speed_RR** | |
| - **Fluid_Temperature** | |
| - **Pedal_Position** | |
| #### 🔠 Categorical Feature: | |
| - **ABS_Status**: `1` = Active, `0` = Inactive | |
| #### 🎯 Target Variable: | |
| - **Fault**: `1` = Fault Detected, `0` = No Fault | |
| """) | |
| # ----------------------------- TAB 2 --------------------------------- | |
| with tab2: | |
| st.title("📊 Exploratory Data Analysis") | |
| st.subheader("📄 View Dataset Preview") | |
| if st.button("🔍 Show Dataset Head"): | |
| st.dataframe(df.head()) | |
| st.subheader("⚠️ Fault Distribution") | |
| fault_counts = df['Fault'].value_counts() | |
| st.bar_chart(fault_counts) | |
| st.write(df['Fault'].value_counts(normalize=True) * 100) | |
| st.subheader("📊 Correlation Heatmap") | |
| corr = df.corr() | |
| fig, ax = plt.subplots(figsize=(10, 8)) | |
| sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", ax=ax) | |
| st.pyplot(fig) | |
| st.markdown("### 📉 Feature Distributions by Fault") | |
| features = ['Brake_Pressure', 'Pad_Wear_Level', 'Wheel_Speed_FL', 'Wheel_Speed_FR', | |
| 'Wheel_Speed_RL', 'Wheel_Speed_RR', 'Fluid_Temperature', 'Pedal_Position'] | |
| for feature in features: | |
| st.markdown(f"#### 🔍 {feature}") | |
| fig, ax = plt.subplots() | |
| sns.kdeplot(data=df, x=feature, hue="Fault", fill=True, common_norm=False, alpha=0.4, ax=ax) | |
| st.pyplot(fig) | |
| st.markdown("### 📦 Boxplots to Compare Fault vs Normal") | |
| for feature in features: | |
| st.markdown(f"#### 📦 {feature} vs Fault") | |
| fig, ax = plt.subplots() | |
| sns.boxplot(data=df, x='Fault', y=feature, palette="Set2", ax=ax) | |
| st.pyplot(fig) | |
| st.markdown("### 📍 Scatterplots: Detect Patterns or Anomalies") | |
| st.markdown("These help you check combinations of features with color-coded fault info.") | |
| fig, ax = plt.subplots() | |
| sns.scatterplot(data=df, x="Brake_Pressure", y="Pad_Wear_Level", hue="Fault", palette="Set1", ax=ax) | |
| ax.set_title("Brake Pressure vs Pad Wear Level") | |
| st.pyplot(fig) | |
| fig, ax = plt.subplots() | |
| sns.scatterplot(data=df, x="Pedal_Position", y="Fluid_Temperature", hue="Fault", palette="Set2", ax=ax) | |
| ax.set_title("Pedal Position vs Fluid Temperature") | |
| st.pyplot(fig) | |
| # ----------------------------- TAB 3 --------------------------------- | |
| with tab3: | |
| st.markdown( | |
| """ | |
| <style> | |
| .stApp { | |
| background-color: #e3f2fd; | |
| padding: 12px; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| st.markdown("## 🚗 Vehicle Brake System Fault Detection") | |
| st.markdown("#### Enter Brake Sensor Values to Predict Any System Fault") | |
| # Prepare data | |
| X = df.drop("Fault", axis=1) | |
| y = df["Fault"] | |
| # UI for user input | |
| Brake_Pressure = st.slider("💨 Brake Pressure (psi)", 50.0, 500.0, step=0.1) | |
| Pad_Wear_Level = st.slider("🛞 Pad Wear Level (%)", 0.0, 100.0, step=0.1) | |
| ABS_Status = st.slider("🛑 ABS Status (0 = Off, 1 = On)", 0, 1, step=1) | |
| Wheel_Speed_FL = st.slider("⚙️ Wheel Speed FL (km/h)", 0.0, 400.0, step=0.1) | |
| Wheel_Speed_FR = st.slider("⚙️ Wheel Speed FR (km/h)", 0.0, 400.0, step=0.1) | |
| Wheel_Speed_RL = st.slider("⚙️ Wheel Speed RL (km/h)", 0.0, 300.0, step=0.1) | |
| Wheel_Speed_RR = st.slider("⚙️ Wheel Speed RR (km/h)", 0.0, 300.0, step=0.1) | |
| Fluid_Temperature = st.slider("🌡️ Fluid Temperature (°C)", -20.0, 150.0, step=0.1) | |
| Pedal_Position = st.slider("🦶 Pedal Position (%)", 0.0, 100.0, step=0.1) | |
| # Train model | |
| x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=29) | |
| model = LogisticRegression() | |
| model.fit(x_train, y_train) | |
| user_input = pd.DataFrame([[Brake_Pressure, Pad_Wear_Level, ABS_Status, Wheel_Speed_FL, | |
| Wheel_Speed_FR, Wheel_Speed_RL, Wheel_Speed_RR, | |
| Fluid_Temperature, Pedal_Position]], | |
| columns=X.columns) | |
| if st.button("🔍 Predict Brake Fault"): | |
| y_pred = model.predict(user_input) | |
| prob = model.predict_proba(user_input)[0][1] | |
| if y_pred[0] == 1: | |
| st.error(f"🚨 Fault Detected in Brake System! (Confidence: {prob:.2%})") | |
| issues = [] | |
| if Brake_Pressure < 60 or Brake_Pressure > 130: | |
| issues.append("🔴 Abnormal Brake Pressure") | |
| if Pad_Wear_Level >= 80: | |
| issues.append("🟠 Brake Pads Critically Worn") | |
| elif Pad_Wear_Level >= 60: | |
| issues.append("🟡 Brake Pads Heavily Worn") | |
| if ABS_Status == 0: | |
| issues.append("🔵 ABS System Not Active") | |
| if Wheel_Speed_FL < 0 or Wheel_Speed_FL > 130: | |
| issues.append("🔴 Front Left Wheel Speed Abnormal") | |
| if Wheel_Speed_FR < 0 or Wheel_Speed_FR > 130: | |
| issues.append("🔴 Front Right Wheel Speed Abnormal") | |
| if Wheel_Speed_RL < 0 or Wheel_Speed_RL > 130: | |
| issues.append("🔴 Rear Left Wheel Speed Abnormal") | |
| if Wheel_Speed_RR < 0 or Wheel_Speed_RR > 130: | |
| issues.append("🔴 Rear Right Wheel Speed Abnormal") | |
| if Fluid_Temperature < -20 or Fluid_Temperature > 120: | |
| issues.append("🔥 Abnormal Brake Fluid Temperature") | |
| if 20 < Pedal_Position < 60: | |
| issues.append("🟡 Moderate Brake Pedal Pressed") | |
| if 60 <= Pedal_Position <= 100: | |
| issues.append("🛑 Brake Pedal Fully Pressed") | |
| if Pedal_Position <= 20: | |
| issues.append("🔍 Low Brake Pedal Engagement") | |
| for issue in issues: | |
| st.markdown(f"- {issue}") | |
| else: | |
| st.success(f"✅ No Fault Detected. (Confidence: {1 - prob:.2%})") | |
| st.info("🚗 Your vehicle's brake system appears healthy.") | |