## 4 Proposed Fault Diagnosis Algorithm
In order to overcome the problem of overfitting and improving generalization, particularly in a small set of data base, the performance function can be modified by: \(msereg=qmse+(1-q)msw\), where \(q\) is the performance ratio, and \(msw\) is the mean of the sum of squares of the network weights and biases. As mentioned in the help section of MATLAB(r), using this performance function causes the network to have smaller weights and biases, and this drives the network response to be smoother and less probable to overfit. Another method to  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Load** & **Speed** & **Torque** & \(f_{s}\) & **Slip** & \(f_{brb}\) & **(Depth, Node)** & \begin{tabular}{c} **Frequency** \\ **Sub-Band** \\ \end{tabular}  \\ \hline High & 958 & 5.7 & 50.01 & 0.042 & 45.811 & (6,3) & [31; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160; 161; 162; 163; 164; 165; 166; 167; 168; 169; 170; 171; 172; 173; 174; 175; 176; 177; 178; 179; 180; 181; 182; 183; 184; 185; 186; 187; 188; 189; 190; 191; 192; 193; 194; 195; 196; 197; 198; 199; 200; 201; 202; 203; 204; 205; 206; 207; 208; 209; 210; 211; 213; 214; 215; 216; 217; 218; 219; 223; 224; 225; 226; 227; 228; 229; 230; 231; 232; 233; 234; 235; 236; 237; 238; 239; 240; 241; 242; 243; 244; 245; 246; 247; 248; 249; 250; 251; 252; 253; 254; 255; 256; 257; 258; 259; 260; 261; 262; 263; 264; 265; 266; 267; 268; 269; 270; 271; 272; 273; 274; 275; 276; 277; 278; 279; 280; 281; 282; 283; 284; 285; 286; 287; 288; 289; 288; 289; 290; 291; 292; 293; 294; 295; 296; 297; 298; 299; 300; 301; 302; 303; 304; 305; 306; 307; 308; 309; 310; 311; 324; 331; 335; 336; 337; 310; 311; 325; 338; 339; 326; 338; 339; 333; 340; 307; 308; 311; 339; 341; 342; 343; 344; 35 evaluate how well the neural network model is, is the Cross-Validation (CV) procedure. It is also very useful for small datasets, since it allows one to use the entire dataset for training and testing. It was found that four fully connected networks each constitutes a separate category of fault number with four output nodes give [0.1; 0.1; 0.1; 0.9] for detecting healthy rotor bar (HRB), [0.1; 0.1; 0.9; 0.1] for the case of one broken rotor bar (1 BRB), [0.1; 0.9; 0.1; 0.1] for two broken rotor bars (2 BRB), and [0.9; 0.1; 0.1; 0.1] are donated to three broken rotor bars (3 BRB), that function as flags to indicate the severity level and the objective is to classify test datasets to the corresponding severity class. In real industrial situation when the stator current of motor is acquired, if its related slip is within the intervals [0.03-0.042], 'high load' text is demonstrated and NN1 will run, which is related to high load (80% of full load), to classify each recording in healthy or faulty mode and assign the resultant severity in faulty case. Otherwise, if the slip is in [0.02-0.026], or [0.008-0.013], or [0.002-0.006], it displays 'Medium Load', 'Low Load', and 'No Load' message and runs NN2, NN3, or NN4, respectively.