An edge oriented architecture for state based remaining useful life prediction in hydraulic systems

Discover Mechanical Engineering, vol. 5

Abstract

Abstract Predictive maintenance in hydraulic systems is constrained in practice by the absence of run-to-failure data, limited component-level observability, and disconnected data pipelines between PLC-level operational events and machine-learning-based prognostics. This paper presents an edge-oriented prototype architecture that integrates these elements into a prototype-level state-based predictive maintenance workflow for hydraulic and fluid power systems. The central methodological contribution is a formal state-transition Remaining Useful Life (RUL) formulation, which defines RUL as the remaining time, or number of hydraulic cycles, before the system transitions from nominal to PLC-supported abnormal or critical operation. This definition provides an operational prognostic target without requiring complete run-to-failure trajectories. Supporting this formulation, an OPC UA-based acquisition and ETL framework aligns high-frequency hydraulic sensor data, multi-resolution storage, and PLC-based event logs into a structured data backbone for the investigated prototype. The framework is evaluated on a real PLC-controlled electro-hydraulic prototype operated across 15 repeated sessions at 100 ms resolution. Latent operating states are inferred using K-Means and Hidden Markov Models and evaluated against PLC-based operational reference logs. K-Means achieved an accuracy of 0.737 and a binary anomaly F1-score of 0.938, while the HMM achieved an accuracy of 0.749, a macro F1-score of 0.635, and a binary anomaly F1-score of 0.933. For RUL estimation, XGBoost achieved a test MAE of 3.916 min and a median prognostic horizon of 34.068 min for the first abnormal transition ( $$R^2=0.911$$ ), while LSTM-Attention achieved the best critical RUL performance with a test MAE of 3.467 min, a median prognostic horizon of 39.662 min, and $$R^2=0.964$$ . SHAP, PDP, and attention-based analyses indicated that the models relied mainly on hydraulically plausible variables, including cylinder velocity, pressure ratios, oil flow rate, and cycle-level descriptors, supporting physical consistency without claiming causal inference. The results provide prototype-level evidence that structured event logging, unsupervised state modeling, and state-transition RUL estimation can be integrated into a coherent hydraulic PdM architecture under practically relevant PLC-integrated constraints, including the absence of run-to-failure labels and restricted component-level observability.

Authors 5

1. Faras Brumand‐Poor corresponding Aachen Institute for Fluid Power Drives and Systems

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Fluid Power Drives and Systems (ifas), Aachen, Germany

2. Manuel Caipo corresponding

   Furtwangen University

   Affiliation as printed

   Faculty of Engineering and Technology, Hochschule Furtwangen University, Villingen-Schwenningen, Germany

3. Jörn Kretschmer corresponding

   Furtwangen University

   Affiliation as printed

   Faculty of Engineering and Technology, Hochschule Furtwangen University, Villingen-Schwenningen, Germany

4. Maximilian Dommermuth corresponding

   Bosch Rexroth (Czechia)

   Affiliation as printed

   Bosch Rexroth Academy, Bosch Rexroth AG, Lohr am Main, Germany

5. Prof. Katharina Schmitz corresponding Aachen Institute for Fluid Power Drives and Systems

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Fluid Power Drives and Systems (ifas), Aachen, Germany