Citation:

Pradip Baral, 2026. Intelligent Failure Prediction in Enterprise Systems Using Behavioural Pattern Modelling,  Volume 6 Issue 3: 84-92.

Abstract:

Intelligent failure prediction in enterprise systems has advanced, from threshold-based failure predictions to modelling behaviours across logs, metrics, traces, storage telemetry, workload rhythms and signs of service interactions. The review is limited to peer-reviewed papers published in journals or conference proceedings from the year 2015 to 2024 and emphasizes on the following areas: operational anomaly detection (OAD), predictive maintenance (PM), system-log reliability analysis (SLR), multivariate time-series modelling and failure oriented interpretation. Reviewed evidence demonstrates that having capability to integrate recurring operational sequences, temporal deviations, seasonal key performance indicators, degradation trajectories and component-level health signals in the same behavioural model contributes to an improvement in early warning capability, because they are normally considered as separate warnings. The literature, however, is spread across cloud computing, microservice observability, industrial prognostics, hard-drive failure prediction, and general anomaly detection. Persistent limitations include weak cross-system generalization, limited labelled data, unstable log templates, limited causal explanation, missing failure labels, and insufficient evaluation under real enterprise drift. More interpretable, topology-aware, and/or temporally calibrated models that relate deviations in behaviour to concrete actionable failure risk instead of the post hoc flagging of anomalies are needed in the field.

References:

[1] He, S., He, P., Chen, Z., Yang, T., Su, Y., & Lyu, M. R. (2021). A survey on automated log analysis for reliability engineering. ACM Computing Surveys, 54(6), Article 130.

[2] Soldani, J., Tamburri, D. A., & Van Den Heuvel, W.-J. (2018). The pains and gains of microservices: A systematic grey literature review. Journal of Systems and Software, 146, 215–232.

[3] Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2021). A review on outlier/anomaly detection in time series data. ACM Computing Surveys, 54(3), Article 56.

[4] Pang, G., Shen, C., Cao, L., & Van Den Hengel, A. (2021). Deep learning for anomaly detection: A review. ACM Computing Surveys, 54(2), Article 38.

[5] Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, Article 106024.

[6] Farshchi, M., Schneider, J.-G., Weber, I., & Grundy, J. (2018). Experience report: Anomaly detection of cloud application operations using log and cloud metric correlation analysis. Journal of Systems and Software, 137, 531–549.

[7] Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015). Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics, 11(3), 812–820.

[8] Xu, C., Wang, G., Liu, X., Guo, D., & Liu, T.-Y. (2016). Health status assessment and failure prediction for hard drives with recurrent neural networks. IEEE Transactions on Computers, 65(11), 3502–3508.

[9] Ahmad, S., Lavin, A., Purdy, S., & Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, 134–147.

[10] Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45.

[11] Munir, M., Siddiqui, S. A., Dengel, A., & Ahmed, S. (2019). DeepAnT: A deep learning approach for unsupervised anomaly detection in time series. IEEE Access, 7, 1991–2005.

[12] Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.

[13] Li, X., Ding, Q., & Sun, J.-Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety, 172, 1–11.

[14] Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834.

[15] Zhang, W., Yang, D., & Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 13(3), 2213–2227.

[16] Cook, A. A., Mısırlı, G., & Fan, Z. (2020). Anomaly detection for IoT time-series data: A survey. IEEE Internet of Things Journal, 7(7), 6481–6494.

[17] Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Müller, K.-R. (2021). A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 109(5), 756–795.

[18] Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. WIREs Data Mining and Knowledge Discovery, 9(3), Article e1316.

[19] Natella, R., Cotroneo, D., & Madeira, H. S. (2016). Assessing dependability with software fault injection: A survey. ACM Computing Surveys, 48(3), Article 44.

[20] Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2021). Deep learning for time series forecasting: A survey. Big Data, 9(1), 3–21.

Keywords:

Anomaly Detection, Behavioural Pattern Modelling, Enterprise Systems, Failure Prediction, Log Analytics, Multivariate Time Series