## 4 Qualitative Analysis of Fault Diagnosis and Predictive Maintenance
### Industrial Compressors: State of the Art and Emerging Trends  
Compressors underpin energy transfer in petrochemical plants, refineries, refrigeration cycles, and diverse manufacturing lines; their failure can halt production and sharply increase operating costs. Consequently, predictive strategies now blend advanced analytics with domain knowledge to anticipate incipient faults and optimise intervention schedules. Based on our expert-LLM screening, the body of work can be organised into five interrelated technique families (Figure 1; Tables S1 and S2). 1. Machine learning (ML). Classical models--linear/polynomial regression, random forest, gradient boosting, kNN, decision trees, SVM, and LDA/QDA--and neural baselines such as MLP and PNN dominate early-stage deployments [29; 30; 31; 32]. Recent studies favour ensembles or stacking (e.g., kNN + gradient boosting with a ridge meta-learner) and anomaly pipelines that combine isolation forest with regression refinements. 2. Deep learning (DL). CNNs extract hierarchical features from raw vibration or spectrogram inputs, sequence models (LSTM, GRU) capture temporal dependencies, and hybrid layouts--TCN-LSTM and attention-augmented autoencoders--boost sensitivity to subtle degradation patterns [33; 34; 35]. Transfer learning from large acoustic datasets is gaining traction where labelled compressor data are scarce. 3. Signal processing and feature engineering. Wavelet packets (MODWPT, DWT), CEEM-DAN, and FFT/STFT remain indispensable for denoising and isolating fault-signature frequencies. Statistical descriptors (RMS, skewness, kurtosis, and variance) feed ML pipelines, while GA, PCA, and kernel PCA support feature selection and dimensionality reduction [36; 37; 38]. 4. Physics-based and hybrid models. Thermofluidodynamic simulations of heat-pump or refrigeration cycles provide virtual sensors for quantities that are difficult to measure in situ; polytropic-exponent or \(p\)-\(V\) correlations help pinpoint valve and leakage defects. Neural surrogates accelerate what-if analyses, replacing expensive CFD or finite-element solvers [39; 40; 38]. 5. Anomaly detection and diagnostics. One-Class SVM, isolation forest, and sequence prediction LSTMs flag outliers and degradation trajectories, enabling early-warning dashboards [41; 42]. Probabilistic outputs are increasingly paired with XAI explanations (e.g., SHAP) to build operator trust. Research converges on six practical themes: (i) fault diagnosis/classification of rotary, reciprocating, centrifugal, and screw units; (ii) predictive maintenance schedulingto minimise downtime; (iii) energy-efficiency optimisation by detecting fouling, leaks, or valve wear; (iv) component degradation profiling of valves, pistons, seals, and bearings; (v) real-time multivariate monitoring in harsh industrial settings; and (vi) IoT/Industry 4.0 integration--edge devices stream high-frequency data to cloud analytics, with results rendered via interactive dashboards. Studies increasingly exploit edge-to-cloud architectures, Apache Kafka pipelines, and time-series databases to handle terabyte-scale vibration/acoustic streams. DL models are compressed with pruning or knowledge distillation for low-latency inference on embedded GPUs. Explainable AI techniques--grad-CAM on spectrogram CNN outputs and feature importance scores for ensemble trees--facilitate root-cause analysis and regulatory compliance. Key challenges include (i) limited availability of labelled datasets covering the full spectrum of compressor types and fault modes, (ii) non-stationary operating conditions that degrade model robustness, and (iii) deployment constraints (latency, memory, and cybersecurity). Promising directions encompass transfer or meta-learning for cross-plant adaptability, domain adaptation methods to bridge synthetic-real data gaps, and digital twins that fuse vibration, acoustic, thermal, and maintenance-log data streams.