Edge-Intelligence Framework for Binary Predictive Maintenance Diagnosis in Industrial Manipulator Workstations
Samuel David Tommy *
Directorate of Works and Engineering Services, Akanu Ibiam Federal Polytechnic, Unwana, Ebonyi, Nigeria.
Thomas Okechukwu Onah
Department of Mechatronics Engineering, Enugu State University of Science and Technology, Agbani, Enugu, Nigeria.
Ndukwe Okoro Agha
Department of Mechanical Engineering Technology, Akanu Ibiam Federal Polytechnic, Unwana, Ebonyi, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Modern industrial manipulators operating within human-in-the-loop environments generate highly variable, noise-contaminated telemetry that can reduce the reliability of centralised predictive maintenance models. Cloud-based diagnostic architectures are constrained by communication bandwidth limitations, security vulnerabilities, and non-deterministic transmission latencies. To address these challenges, this paper introduces an autonomous, licence-free edge-intelligence framework designed for real-time in situ kinematic fault diagnosis without external computational or cloud dependencies. The studied architecture couples an event-driven Node-RED data orchestration engine with a synchronised, multi-channel sliding-window preprocessing pipeline. This architecture was empirically validated on an active production floor using multivariable telemetry, comprising motor current, axial vibration, and joint temperature, captured over a continuous 250-second sequence encompassing 50 distinct human-driven manual execution cycles. Through preprocessing via an 80%-overlapping sliding-window protocol (W = 10 s, S = 2 s), the framework generated 116 serialised feature windows, comprising 80 windows for training and 36 windows for independent testing. Raw telemetry was compressed into a low-dimensional feature space that isolates structural degradation markers from human-induced operational transients. Evaluated using a block-stratified 5-fold cross-validation scheme to prevent temporal data leakage, the native edge-compiled binary decision tree achieved classification accuracy and precision of 100% on the independent evaluation blocks. By restricting the classifier split depth, the diagnostic logic compiles directly into standard nested C-style conditional loops. Hardware profiling on a target ARM Cortex-M7 microcontroller demonstrated ultra-low-overhead execution, requiring an inference latency of less than 1.2 microseconds and a memory footprint below 8 KB, leaving 99.2% of the on-chip RAM free for core control loops. This framework provides a verifiable blueprint for low-overhead, high-precision hardware-in-the-loop implementation of binary fault isolation within a localised mechatronic workstation environment.
Keywords: TinyML, edge intelligence, predictive maintenance, industrial manipulators, condition monitoring, binary fault diagnosis, sliding-window preprocessing, decision tree, embedded systems, shop-floor telemetry.