Fault Diagnosis Implemented with Immune Algorithm

Application of Immune Algorithm in Fault Diagnosis

Fault diagnosis serves as a critical component in industrial fields, with its core objective being the rapid and accurate identification of abnormal states in systems. The Immune Algorithm, an intelligent optimization method inspired by biological immune systems, provides novel solutions for fault diagnosis by simulating the recognition and elimination mechanisms of antibodies against antigens.

Core Logic of Immune Algorithm

Antigen Recognition: Equipment fault signals or abnormal data are analogous to antigens, requiring the algorithm to quickly identify these abnormal patterns. In code implementation, this typically involves feature extraction through signal processing functions like FFT or wavelet transforms.
Antibody Generation: Candidate solutions (antibody population) are generated through random initialization or historical data to match fault characteristics. Programmatically, this involves creating initial solution vectors using random number generators with system constraints.
Affinity Calculation: Evaluates the matching degree between antibodies and antigens (such as error functions or similarity metrics) to screen high-precision solutions. This is implemented through fitness functions calculating Euclidean distance or correlation coefficients between current states and fault patterns.
Cloning and Mutation: High-quality antibodies undergo clonal expansion with controlled mutations to enhance global search capability. Code implementation includes cloning operators that duplicate best-performing solutions and mutation functions introducing small perturbations using Gaussian distribution.
Memory Bank Update: Efficient antibodies are retained to form immune memory, accelerating diagnostic responses for similar future faults. This is achieved through data structures that store optimal solution vectors with timestamp tracking.

Algorithm Advantages and Diagnostic Accuracy

The unique advantage of immune algorithms lies in their parallel search capability and dynamic adaptability. By simulating the diversity maintenance mechanism of biological immune systems, the algorithm effectively avoids local optima, significantly improving complex fault identification rates. For example, in rotating machinery or power system fault diagnosis, immune algorithms combined with vibration signals or current waveform characteristics can achieve over 95% classification accuracy, substantially outperforming traditional threshold-based methods. The algorithm's parallel evaluation structure allows simultaneous testing of multiple fault hypotheses through matrix operations.

Optimization Directions in Practical Applications

Feature Extraction: Combine wavelet transforms or deep learning for preprocessing raw signals to improve antigen representation accuracy. Implementation involves using scipy.signal for feature extraction or TensorFlow for automated feature learning.
Hybrid Strategies: Integrate with Support Vector Machines (SVM) or neural networks to compensate for limitations in handling nonlinear problems. Code integration typically involves using scikit-learn's SVM as a classifier after immune algorithm feature selection.
Real-time Improvements: Meet industrial real-time diagnostic requirements through incremental learning in memory banks and dynamic antibody population pruning. This can be implemented using circular buffers for memory management and adaptive population sizing algorithms.

Immune algorithms provide a biologically inspired intelligent approach to fault diagnosis, with core value in transforming complex fault pattern matching into optimizable computational problems. With advancements in edge computing and lightweight models, future deployment of such algorithms in embedded devices becomes increasingly feasible through C++ implementations with ARM architecture optimization.