Volume-12, Issue-7, July 2026

Abstract

Psychopathy is conventionally assessed through structured clinical interviews and behavioral rating instruments such as the Psychopathy Checklist-Revised (PCL-R), a process that is time-intensive, dependent on rater training, and not scalable for large-cohort or screening use. Advances in electroencephalography (EEG) signal processing, functional magnetic resonance imaging (fMRI) analysis, and deep learning have enabled automated classification pipelines for a range of psychiatric and neurological conditions, raising the question of whether comparable pipelines could support research into the neurocognitive correlates of psychopathic traits. This paper does not report a trained model or empirical classification results; rather, it proposes a conceptual and methodological framework intended to orient future empirical work in this area. We review the current state of AI-assisted neuroimaging classification in adjacent psychiatric domains, identify the specific theoretical and practical obstacles that distinguish psychopathy research from those domains, and propose a four-layer framework spanning theoretical grounding, data and labeling strategy, computational modeling, and validation. We argue that the field's most useful near-term contribution is not a binary psychopath/non-psychopath classifier but a dimensional, explainable, cross-validated modeling approach anchored to established neurocognitive theory. We close with a concrete research agenda and an explicit discussion of the ethical constraints that should shape how such systems are built, evaluated, and reported.

Keywords: psychopathy, EEG, fMRI, neuroimaging, deep learning, explainable AI, multimodal fusion, PCL-R, forensic neuroscience.

References

1. Ahmed, Z., Wali, A., Shahid, S., Zikria, S., Rasheed, J., & Asuroglu, T. (2024). Psychiatric disorders from EEG signals through deep learning models. IBRO Neuroscience Reports, *17*, 300-310. https://doi.org/10.1016/j.ibneur.2024.09.003
2. Blair, R. J. R. (2007). The amygdala and ventromedial prefrontal cortex in morality and psychopathy. Trends in Cognitive Sciences, *11*(9), 387-392. https://doi.org/10.1016/j.tics.2007.07.003
3. Blair, R. J. R. (2008). The amygdala and ventromedial prefrontal cortex: Functional contributions and dysfunction in psychopathy. Philosophical Transactions of the Royal Society B: Biological Sciences, *363*(1503), 2557-2565. https://doi.org/10.1098/rstb.2008.0027
4. Botha, M., & Polaschek, D. L. L. (2012). Psychopathy and the predictive validity of the PCL-R: An international perspective. Behavioral Sciences & the Law, *30*(5), 523-543. https://doi.org/10.1002/bsl.2032
5. Chalkia, E., & Halkiopoulos, C. (2025). From neural networks to emotional networks: A systematic review of EEG-based emotion recognition in cognitive neuroscience and real-world applications. Brain Sciences, *15*(3), 220. https://doi.org/10.3390/brainsci15030220
6. Lyu, R. (2025). Deep learning approaches for EEG-based healthcare applications: A comprehensive review. Frontiers in Human Neuroscience, *19*, 1689073. https://doi.org/10.3389/fnhum.2025.1689073
7. Ma, D., Zhang, H., & Wang, L. (2024). Editorial: Deep learning methods and applications in brain imaging for the diagnosis of neurological and psychiatric disorders. Frontiers in Neuroscience, *18*, 1497417. https://doi.org/10.3389/fnins.2024.1497417
8. Moradizeyveh, S., Hanif, A., Liu, S., Qi, Y., Beheshti, A., & Di Ieva, A. (2025). Eye-guided multimodal fusion: Toward an adaptive learning framework using explainable artificial intelligence. Sensors, *25*(15), 4575. https://doi.org/10.3390/s25154575
9. Naderi, M., & Jahanian-Najafabadi, A. (2025). A systematic review of EEG-based machine learning classifications for obsessive-compulsive disorder: Current status and future directions. BMC Psychiatry, *25*, 7296. https://doi.org/10.1186/s12888-025-07296-z
10. Quaak, M., van de Mortel, L., Thomas, R. M., & van Wingen, G. (2021). Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis. NeuroImage: Clinical, *30*, 102584. https://doi.org/10.1016/j.nicl.2021.102584
11. Rahul, J., Sharma, D., Sharma, L. D., Nanda, U., & Sarkar, A. K. (2024). A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning. Frontiers in Human Neuroscience, *18*, 1347082. https://doi.org/10.3389/fnhum.2024.1347082
12. Risk Management Authority (Scotland). (2023). Psychopathy Checklist-Revised (PCL-R): Evidence summary. https://www.rma.scot/wp-content/uploads/2023/01/Psychopathy-Checklist-Revised-PCL-R.pdf
13. Zhai, X., Li, C., & Wang, Y. (2025). A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: Trends in explainable artificial intelligence (XAI). BMC Psychiatry, *26*, 90. https://doi.org/10.1186/s12888-025-07589-3. 

Abstract

The increasing adoption of smart manufacturing technologies has intensified the need for reliable predictive maintenance solutions to reduce unexpected equipment failures and production downtime. Rotating machinery, including bearings, electric motors, gearboxes, pumps, and turbines, represents a critical class of industrial assets whose degradation directly affects manufacturing productivity, operational safety, and maintenance costs. This study presents a trustworthy AI-driven framework for condition monitoring and Remaining Useful Life (RUL) prediction of rotating machinery in smart manufacturing environments. The proposed framework integrates multi-sensor condition monitoring using vibration, temperature, acoustic emission, pressure, and motor current measurements with machine learning and deep learning models for intelligent fault diagnosis and prognostics. To improve transparency and industrial trust, the framework incorporates Explainable Artificial Intelligence (XAI) techniques, including SHAP and LIME, to identify the operational factors influencing prediction outcomes. In addition, Digital Twin technology and Physics-Informed Artificial Intelligence are integrated to enhance prediction reliability and maintain consistency with engineering knowledge. The framework is evaluated using benchmark datasets and standard classification and regression metrics, including Accuracy, Precision, Recall, F1-Score, MAE, RMSE, and RUL prediction error. The results demonstrate that the integration of multi-sensor monitoring, explainable AI, and Digital Twin-assisted analysis improves diagnostic reliability, prediction consistency, and maintenance decision support. The proposed approach offers practical benefits for Industry 4.0 applications by reducing unplanned downtime, optimizing maintenance scheduling, improving equipment availability, and enhancing the trustworthiness of AI-based predictive maintenance systems for critical rotating machinery.

Keywords: Trustworthy Artificial Intelligence, Predictive Maintenance, Condition Monitoring, Remaining Useful Life Prediction, Rotating Machinery, Smart Manufacturing, Industry 4.0, Explainable Artificial Intelligence, Digital Twin, Physics-Informed AI, Machine Learning, Deep Learning.

References

1. Antoni, J. (2006). The spectral kurtosis: A useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing, *20*(2), 282-307.
2. Armstrong, N. (1987). Desire and domestic fiction: A political history of the novel. Oxford University Press.
3. Beauvoir, S. de. (1989). The second sex (H. M. Parshley, Trans.). Vintage Books. (Original work published 1949)
4. Breiman, L. (2001). Random forests. Machine Learning, *45*(1), 5-32.
5. Brontë, C. (1847). Jane Eyre. Smith, Elder & Co.
6. Caesarendra, W., & Tjahjowidodo, B. (2015). A review of feature extraction methods in condition monitoring and machine fault diagnosis. Measurement, *69*, 148-156.
7. Cerrada, M., Zurita, G., Cabrera, D., Sánchez, R., Li, C., & de Oliveira, R. V. (2020). Fault diagnosis in rotating machinery using signal processing and machine learning techniques. IEEE Access, *8*, 212728-212748.
8. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
9. Cho, K., et al. (2014). Learning phrase representations using RNN encoder–decoder for statistical machine translation. Proceedings of EMNLP, 1724-1734.
10. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, *20*(3), 273-297.
11. Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, *13*(1), 21-27.
12. Davidoff, L., & Hall, C. (1987). Family fortunes: Men and women of the English middle class, 1780–1850. Hutchinson.
13. Deutsch, J., & He, D. (2018). Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Transactions on Systems, Man, and Cybernetics: Systems, *48*(1), 11-20.
14. dos Santos, F. B., de Souza, R. B. M., & Torres, L. H. S. (2019). Machine learning techniques applied to predictive maintenance: A systematic literature review. Computers & Industrial Engineering, *137*, Article 106024.
15. Eagleton, T. (1975). Myths of power: A Marxist study of the Brontës. Macmillan Press.
16. Eagleton, T. (1976). Marxism and literary criticism. University of California Press.
17. FEMTO-ST Institute. (2012). PRONOSTIA bearing accelerated life test data set.
18. Gilbert, S. M., & Gubar, S. (1979). The madwoman in the attic: The woman writer and the nineteenth-century literary imagination. Yale University Press.
19. Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, *23*(3), 724-739.
20. Hinton, G. E., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv, arXiv:1503.02531.
21. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, *9*(8), 1735-1780.
22. Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, *20*(7), 1483-1510.
23. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, *60*(6), 84-90.
24. Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, *56*(1-2), 508-517.
25. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, *521*(7553), 436-444.
26. Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, *3*, 18-23.
27. Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2016). Prognostics and health management design for smart manufacturing systems. Manufacturing Letters, *8*, 31-34.
28. Lei, C., Li, N., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to remaining useful life prediction. Mechanical Systems and Signal Processing, *104*, 799-834.
29. Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2017). Machinery health prognostics: State of the art and future trends. Mechanical Systems and Signal Processing, *94*, 173-203.
30. Lei, Y., Li, N., & Lin, J. (2016). A new method based on stochastic process models for machine remaining useful life prediction. IEEE Transactions on Instrumentation and Measurement, *65*(12), 2671-2684.
31. Li, Y., Ding, X., & Liu, Z. (2020). Remaining useful life prediction using deep long short-term memory networks. Mechanical Systems and Signal Processing, *138*, Article 106570.
32. Marx, K. (1990). Capital: A critique of political economy (Vol. 1). Penguin Classics. (Original work published 1867)
33. Marx, K., & Engels, F. (2002). The Communist manifesto. Penguin Classics. (Original work published 1848)
34. Mobley, R. K. (2002). An introduction to predictive maintenance (3rd ed.). Elsevier.
35. Moglen, H. (1976). Charlotte Brontë: The self conceived. W. W. Norton & Company.
36. Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors—A review. IEEE Transactions on Energy Conversion, *20*(4), 719-729.
37. Paderborn University. (2020). Paderborn bearing data center.
38. Poovey, M. (1988). Uneven developments: The ideological work of gender in mid-Victorian England. University of Chicago Press.
39. Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics—A tutorial. Mechanical Systems and Signal Processing, *25*(2), 485-520.
40. Rhys, J. (1966). Wide Sargasso Sea. André Deutsch.
41. Rich, A. (1979). Jane Eyre: The temptations of a motherless woman. In On lies, secrets, and silence: Selected prose 1966–1978 (pp. 89-106). W. W. Norton & Company.
42. Rigby, E. (1848). Vanity Fair—and Jane Eyre. Quarterly Review, *84*(167), 153-185.
43. Saxena, A., & Goebel, K. (2008). Turbofan engine degradation simulation data set. NASA Ames Prognostics Data Repository.
44. Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. International Conference on Prognostics and Health Management.
45. Showalter, E. (1977). A literature of their own: British women novelists from Brontë to Lessing. Princeton University Press.
46. Spivak, G. C. (1985). Three women's texts and a critique of imperialism. Critical Inquiry, *12*(1), 243-261.
47. Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber–physical systems toward smart manufacturing and Industry 4.0: Correlation and comparison. Engineering, *5*(4), 653-661.
48. Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, *30*, 5998-6008.
49. Wang, H., Liang, P., & Zhang, X. (2020). Hybrid CNN–LSTM network for remaining useful life prediction of rotating machinery. IEEE Access, *8*, 14324-14336.
50. Wang, S., Xiang, J., Zhong, Y., & Tang, H. (2021). A data-driven intelligent fault diagnosis method for rotating machinery. IEEE Transactions on Industrial Electronics, *68*(10), 10430-10441.
51. Wollstonecraft, M. (1988). A vindication of the rights of woman. Norton. (Original work published 1792)
52. Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, *56*(8), 2941-2962.
53. Yin, S., & Kaynak, O. (2015). Big data for modern industry: Challenges and trends. Proceedings of the IEEE, *103*(2), 143-146.
54. Zhang, K., Tang, B., Deng, X., & Huang, W. (2021). Remaining useful life prediction based on deep learning: A survey. Reliability Engineering & System Safety, *211*, Article 107587.
55. Zhang, P., Qin, Y., Wang, D., & Zhang, H. (2021). Intelligent condition monitoring of rotating machinery: Recent advances and future trends. IEEE Access, *9*, 155214-155236.
56. Zhang, X., Ge, Z., & Song, Z. (2022). Data-driven monitoring technologies for smart manufacturing. IEEE Transactions on Industrial Informatics, *18*(3), 1847-1859.
57. Zhao, Z., Liang, B., Wang, X., & Lu, W. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, *115*, 213-237.