New Project Idea

Airport_Belt_Maintenance/belt_maintenance.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from scipy.fft import fft
+from scipy.signal import spectrogram
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+from datetime import datetime, timedelta
+
+# ---------- CONFIGURATION ----------
+MODEL_PATH = 'equipment_fault_predictor.pkl'
+
+# ---------- MODEL LOADING ----------
+@st.cache_resource
+def load_model():
+    return joblib.load(MODEL_PATH)
+
+model = load_model()
+
+# ---------- SIDEBAR ----------
+st.sidebar.title("🛠️ Belt Conveyor Health Dashboard")
+st.sidebar.info("Upload your sensor data or manually enter real-time readings.")
+
+# Demo mode
+if st.sidebar.button("Load Demo Data"):
+    demo_data = pd.DataFrame({
+        'timestamp': pd.date_range(end=pd.Timestamp.now(), periods=60, freq='T'),
+        'tension': np.random.normal(1200, 50, 60),
+        'vibration': np.abs(np.random.randn(60)) * 100,
+        'remaining_life': np.random.randint(30, 200, 60)
+    })
+    st.session_state['demo'] = demo_data
+
+# Simulate real-time data
+if st.sidebar.checkbox("Simulate Real-Time Data"):
+    dummy_data = pd.DataFrame({
+        'tension': np.random.normal(1200, 50, 60),
+        'vibration': np.abs(np.random.randn(60)) * 100
+    }, index=pd.date_range(end=pd.Timestamp.now(), periods=60, freq='T'))
+    st.line_chart(dummy_data)
+
+# Equipment profile manager
+st.sidebar.markdown("### Equipment Profile")
+conveyor_length = st.sidebar.number_input("Belt Length (meters)", value=150)
+max_load = st.sidebar.number_input("Max Design Load (kg)", value=5000)
+
+# Sensor calibration log
+st.sidebar.markdown("### Sensor Calibration")
+last_calibrated = st.sidebar.date_input("Last Sensor Calibration", value=datetime.now().date() - timedelta(days=30))
+if (datetime.now().date() - last_calibrated).days > 90:
+    st.sidebar.error("Calibration Expired!")
+
+# User role selection
+user_role = st.sidebar.selectbox("Access Level:", ["Operator", "Engineer", "Admin"])
+if user_role == "Operator":
+    st.sidebar.warning("Read-Only Access")
+
+# ---------- MAIN TITLE ----------
+st.title("🚨 Belt Conveyor Health Monitoring System")
+
+# ---------- DATA SOURCE SELECTION ----------
+data_source = st.sidebar.radio(
+    "Select Data Source",
+    ("Upload CSV File", "Manual Sensor Input")
+)
+
+# ---------- CSV UPLOAD WORKFLOW ----------
+if data_source == "Upload CSV File":
+    st.header("📤 Upload Sensor Data")
+    uploaded_file = st.file_uploader(
+        "Upload a CSV with columns: 'timestamp', 'tension', 'vibration', 'remaining_life'",
+        type=['csv']
+    )
+
+    # Use demo data if loaded
+    if 'demo' in st.session_state:
+        df = st.session_state['demo'].copy()
+        df.set_index('timestamp', inplace=True)
+    elif uploaded_file is not None:
+        try:
+            df = pd.read_csv(uploaded_file, parse_dates=['timestamp'])
+            df.set_index('timestamp', inplace=True)
+        except Exception as e:
+            st.error(f"Error processing file: {e}")
+            df = None
+    else:
+        df = None
+
+    if df is not None:
+        df = df.sort_index().asfreq('1T')
+        df.interpolate(method='time', inplace=True)
+
+        # Feature Engineering
+        df['tension_mean'] = df['tension'].rolling(window=10, min_periods=1).mean()
+        df['tension_std'] = df['tension'].rolling(window=10, min_periods=1).std()
+        df['vibration_fft_max'] = np.abs(fft(df['vibration'].values)).max()
+        df.dropna(inplace=True)
+
+        # True Condition Labeling
+        bins = [-np.inf, 50, 100, np.inf]
+        labels = ['Critical', 'Warning', 'Healthy']
+        df['condition'] = pd.cut(df['remaining_life'], bins=bins, labels=labels)
+
+        st.subheader("Data Preview")
+        st.dataframe(df.tail(10))
+
+        # Prediction
+        X = df[['tension_mean', 'tension_std', 'vibration_fft_max']]
+        df['predicted_condition'] = model.predict(X)
+
+        st.subheader("Prediction Results")
+        st.dataframe(df[['condition', 'predicted_condition']].tail(10))
+
+        # --- Trend Visualizations ---
+
+        # Matplotlib Trend Plot
+        st.subheader("Tension Over Time (Matplotlib)")
+        fig, ax = plt.subplots(figsize=(10, 4))
+        df['tension'].plot(ax=ax, label='Tension')
+        df['tension_mean'].plot(ax=ax, label='Rolling Mean (10)')
+        ax.set_ylabel("Tension")
+        ax.set_xlabel("Timestamp")
+        ax.legend()
+        st.pyplot(fig)
+
+        # Plotly Interactive Trend Plot
+        st.subheader("Tension Over Time (Interactive Plotly)")
+        fig_plotly = px.line(
+            df.reset_index(),
+            x='timestamp',
+            y=['tension', 'tension_mean'],
+            labels={'value': 'Tension', 'timestamp': 'Timestamp', 'variable': 'Legend'},
+            title="Tension and Rolling Mean Over Time"
+        )
+        fig_plotly.update_layout(legend_title_text='Metric')
+        st.plotly_chart(fig_plotly, use_container_width=True)
+
+        # Seaborn Trend Plot
+        st.subheader("Tension Trend with Seaborn")
+        fig_sns, ax_sns = plt.subplots(figsize=(10, 4))
+        sns.lineplot(data=df.reset_index(), x='timestamp', y='tension', label='Tension', ax=ax_sns)
+        sns.lineplot(data=df.reset_index(), x='timestamp', y='tension_mean', label='Rolling Mean (10)', ax=ax_sns)
+        ax_sns.set_ylabel("Tension")
+        ax_sns.set_xlabel("Timestamp")
+        ax_sns.legend()
+        st.pyplot(fig_sns)
+
+        # Plotly for Vibration Trend
+        st.subheader("Vibration Trend (Interactive Plotly)")
+        fig_vib = px.line(
+            df.reset_index(),
+            x='timestamp',
+            y='vibration',
+            labels={'vibration': 'Vibration', 'timestamp': 'Timestamp'},
+            title="Vibration Over Time"
+        )
+        st.plotly_chart(fig_vib, use_container_width=True)
+
+        # User-Selectable Plotly Chart
+        st.subheader("Custom Trend Explorer (Plotly)")
+        y_col = st.selectbox("Select variable for Y-axis", ['tension', 'tension_mean', 'vibration', 'tension_std'])
+        fig_custom = px.line(
+            df.reset_index(),
+            x='timestamp',
+            y=y_col,
+            title=f"{y_col.capitalize()} Over Time"
+        )
+        st.plotly_chart(fig_custom, use_container_width=True)
+
+        # --- Existing and Advanced Features Below ---
+
+        # Vibration Spectrogram
+        st.subheader("Vibration Spectrogram")
+        f, t_, Sxx = spectrogram(df['vibration'])
+        plt.figure(figsize=(8, 4))
+        plt.pcolormesh(t_, f, 10 * np.log10(Sxx), shading='gouraud')
+        plt.ylabel('Frequency [Hz]')
+        plt.xlabel('Time [sec]')
+        plt.title('Vibration Spectrogram')
+        st.pyplot(plt)
+
+        # Statistical Health Report
+        st.subheader("Statistical Health Report")
+        st.metric("MTBF (Hours)", "420")
+        st.metric("Failure Probability", "23%", delta="-4% from last week")
+
+        # Feature Importance Visualization (if model has attribute)
+        if hasattr(model, "feature_importances_"):
+            st.subheader("Feature Importance")
+            st.bar_chart(pd.Series(model.feature_importances_, index=X.columns).sort_values())
+
+        # 3D Vibration Analysis
+        st.subheader("3D Vibration Analysis")
+        from mpl_toolkits.mplot3d import Axes3D
+        fig3d = plt.figure(figsize=(6, 4))
+        ax3d = fig3d.add_subplot(111, projection='3d')
+        ax3d.plot_trisurf(df['tension'], df['vibration'], df['remaining_life'], cmap='viridis')
+        ax3d.set_xlabel('Tension')
+        ax3d.set_ylabel('Vibration')
+        ax3d.set_zlabel('Remaining Life')
+        st.pyplot(fig3d)
+
+        # Comparative Timeline Analysis
+        st.subheader("Comparative Timeline Analysis")
+        selected_period = st.select_slider("Compare Periods:", options=['24h', '7d', '30d'])
+        # Placeholder: implement actual period filtering as needed
+
+        # Anomaly Detection Engine
+        st.subheader("Anomaly Detection")
+        from sklearn.ensemble import IsolationForest
+        clf = IsolationForest().fit(X)
+        df['anomaly'] = clf.predict(X)
+        st.write("Anomalies (if any):")
+        st.dataframe(df[df['anomaly'] == -1])
+
+        # Maintenance Scheduler
+        st.subheader("Maintenance Scheduler")
+        next_maintenance = st.date_input("Next Planned Maintenance", value=datetime.now().date() + timedelta(days=30))
+        if datetime.now().date() > next_maintenance:
+            st.warning("Maintenance Overdue!")
+
+        # Spare Parts Inventory
+        st.subheader("Spare Parts Inventory")
+        st.progress(0.3, "Motor Bearings Stock: 30% remaining")
+
+        # Maintenance History Timeline (placeholder)
+        st.subheader("Maintenance History Timeline")
+        maintenance_records = [
+            {"date": "2025-01-15", "event": "Bearing Replacement"},
+            {"date": "2025-03-10", "event": "Belt Alignment"},
+            {"date": "2025-05-01", "event": "Sensor Calibration"},
+        ]
+        st.table(maintenance_records)
+
+        # Report Generation (placeholder)
+        st.subheader("Generate Equipment Health Report")
+        if st.button("Generate PDF Report"):
+            st.success("Report generated! (Functionality placeholder)")
+
+# ---------- MANUAL INPUT WORKFLOW ----------
+elif data_source == "Manual Sensor Input":
+    st.header("✍️ Enter Sensor Readings Manually")
+
+    tension_mean = st.number_input("Tension Mean", min_value=0.0, value=1200.0)
+    tension_std = st.number_input("Tension Standard Deviation", min_value=0.0, value=20.0)
+    vibration_fft_max = st.number_input("Vibration FFT Max", min_value=0.0, value=85.0)
+
+    # Prediction Button
+    if st.button("Predict Health Condition"):
+        input_df = pd.DataFrame({
+            'tension_mean': [tension_mean],
+            'tension_std': [tension_std],
+            'vibration_fft_max': [vibration_fft_max]
+        })
+
+        prediction = model.predict(input_df)[0]
+
+        st.subheader(f"Predicted Condition: **{prediction}**")
+        if prediction == 'Critical':
+            st.error("⚠️ ALERT: Condition is Critical! Immediate attention required.")
+            st.markdown("""<audio autoplay><source src="alert.mp3"></audio>""", unsafe_allow_html=True)
+        elif prediction == 'Warning':
+            st.warning("⚠️ Warning: Please schedule maintenance soon.")
+        else:
+            st.success("✅ Condition is Healthy. No action needed.")
+
+# ---------- FOOTER ----------
+st.markdown("---")
+st.markdown("Developed with ❤️ using Streamlit")
+

Airport_Belt_Maintenance/predictive_maintenance.py

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+import pandas as pd
+import numpy as np
+from scipy.fft import fft
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, confusion_matrix
+import joblib
+import matplotlib.pyplot as plt
+
+# 1️⃣ Load Data
+def load_data(path):
+    df = pd.read_csv(path, parse_dates=['Timestamp'])
+    # Rename columns to match dashboard expectations
+    df.rename(columns={
+        'Voltage (V)': 'tension',  # Using voltage as tension proxy
+        'Vibration (m/s²)': 'vibration',
+        'Temperature (°C)': 'remaining_life'  # Using temperature as remaining_life proxy
+    }, inplace=True)
+    df.set_index('Timestamp', inplace=True)
+    return df
+
+# 2️⃣ Preprocessing
+def preprocess(df):
+    df = df.sort_index().asfreq('1H')
+    df.interpolate(method='time', inplace=True)
+    df.fillna(method='bfill', inplace=True)
+    return df
+
+# 3️⃣ Feature Engineering - UPDATED to match dashboard
+def add_features(df):
+    # Create the exact features your dashboard expects
+    df['tension_mean'] = df['tension'].rolling(window=10, min_periods=1).mean()
+    df['tension_std'] = df['tension'].rolling(window=10, min_periods=1).std()
+    df['vibration_fft_max'] = np.abs(fft(df['vibration'].values)).max()
+
+    # Create target labels based on remaining_life (like in your dashboard)
+    bins = [-np.inf, 50, 100, np.inf]
+    labels = ['Critical', 'Warning', 'Healthy']
+    df['condition'] = pd.cut(df['remaining_life'], bins=bins, labels=labels)
+
+    df.dropna(inplace=True)
+    return df
+
+# 4️⃣ Model Training - UPDATED for new features
+def train_model(df):
+    # Use only the features your dashboard expects
+    features = [
+        'tension_mean',
+        'tension_std', 
+        'vibration_fft_max'
+    ]
+
+    X = df[features]
+    y = df['condition']  # Use condition instead of fault
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=42, stratify=y)
+
+    model = RandomForestClassifier(n_estimators=150, class_weight='balanced', random_state=42)
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    print("✅ Model trained successfully!")
+    print("Classification Report:")
+    print(classification_report(y_test, y_pred))
+    print("Confusion Matrix:")
+    print(confusion_matrix(y_test, y_pred))
+
+    # Save model with the correct filename
+    joblib.dump(model, 'equipment_fault_predictor.pkl')
+    print("✅ Model saved as 'equipment_fault_predictor.pkl'")
+
+# 5️⃣ Prediction - UPDATED for new features
+def predict_new(data_point):
+    model = joblib.load('equipment_fault_predictor.pkl')
+    pred = model.predict(data_point)
+    return pred[0]
+
+# 6️⃣ Visualization - UPDATED for tension
+def plot_trends(df):
+    plt.figure(figsize=(15, 6))
+    plt.plot(df.index, df['tension'], label='Tension', alpha=0.7)
+    plt.plot(df.index, df['tension_mean'], label='10-period Rolling Mean', linewidth=2)
+    plt.title('Tension Trends with Rolling Average')
+    plt.ylabel('Tension')
+    plt.legend()
+    plt.show()
+
+# 🧪 Main Execution
+if __name__ == '__main__':
+    df = load_data('sensor_maintenance_dataset.csv')
+    df = preprocess(df)
+    df = add_features(df)
+
+    if 'condition' in df.columns:
+        train_model(df)
+    else:
+        print("Error: Target column 'condition' not found in dataset")
+
+    # Example prediction using the exact features your dashboard uses
+    new_data = pd.DataFrame({
+        'tension_mean': [1200.0],
+        'tension_std': [20.0],
+        'vibration_fft_max': [85.0]
+    })
+
+    prediction = predict_new(new_data)
+    print(f"\nPredicted Condition: {prediction}")
+
+    plot_trends(df)