AI Powered Maintenance Forecasting in Mechanical Systems: A Data Driven Approach

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Introduction

Integration of Artificial Intelligence in predictive maintenance systems constitutes a breakthrough in

mechanical apparatus oversight and functionality (Abbas, 2024). Industry 4.0 has showcased that

there are weaknesses in conventional maintenance approaches, and unforeseen equipment

breakdowns have cost manufacturers globally $1.4 trillion annually (Business Insider, 2025). Such

situations of dormancy not only inflate operational costs but also compromise security and supply

chain integrity. On this front, AI driven predictive maintenance (PdM) has become an advanced

process that predicts equipment breakdowns and allows data-driven scheduling of maintenance

operations.

Modern predictive maintenance (PdM) systems leverage high-resolution, multi-modal sensor data,

including vibration, temperature, current consumption, and ultrasonic sounds, supplemented by

advanced artificial intelligence architectures. These architectures include ensemble methods, such as

Gradient Boosting and Random Forests, and deep learning networks, and more recently, large

language models (LLMs) used to contextualize and explain outlier patterns (ResearchGate, 2025).

LLMs improve PdM systems by analyzing unstructured data—i.e., maintenance records and operator

comments—thus making predictive analytics both accurate and explainable (Algomox, 2025).

Edge AI usage is all the more common in time-sensitive industrial environments to enable real-time

analytics on-site and maintain privacy by reducing reliance on cloud connectivity (Preprints, 2025).

For power grids, rail networks, and factories, AI agents based at the edge (such as Avangrid's assistant

autopilot) can trigger maintenance processes independently and at high speeds.

Despite this, the development of AI-based predictive maintenance (PdM) still faces numerous

challenges. These include heterogeneous sensor environments, data integrity concerns, regulatory

compliance, and resistance from technicians due to differences in workforce capabilities (Insider,

2025). Strategic approaches include the use of robust data pipelines, flexible AI architectures, and co-

design with domain experts to improve acceptability and credibility (Algomox, 2025; FT, 2024).

This research positions itself at the nexus of these advancements. By combining sensor fusion, machine

learning ensembles, LLM-driven reasoning, and edge AI deployment, we seek to advance PdM from

reactive to prescriptive maintenance. We contextualize our framework using recent industrial

benchmarks, compare AI architectures and including TranDRL-style transformers and LLM

augmented systems, and validate using real world inspired datasets.

Advancements, ROI, and Challenges

1. Traditional maintenance methods, in particular reactive and preventive maintenance, are

increasingly becoming unsustainable because they have high ownership costs. Combinations

of frequent inspections and deferred reactive repairs all worsen inefficiencies, leading to more

than 20% downtime compared to smart systems. These approaches show weaknesses in their

ability to monitor intra-system changes in real-time, often resulting in sub-optimal decision-

making and high costs.

2. The emergence of artificial intelligence-aided predictive maintenance (PdM), enabled by

heterogeneous sensor arrays and machine learning tools, has delivered high return on

investment (ROI). A global survey in 2025 reported that manufacturing businesses deploying

AI-enabled PdM systems saw the frequency of unplanned downtime reduced by 37%,

expenditure on maintenance dropped by 28%, and equipment lifespan increased by 22%, with

investment recovery achieved in a maximum of 14 months. Other industrial evaluations record

improvements in predictive accuracy of 20% to 30%, along with downtimes reduced by as

much as 45%, thus heralding the revolutionary impact of intelligent systems.

3. The importance of Edge AI has significantly grown because of its ability to process

information at the edge, which reduces latency and compliance risks. Modern frameworks take

advantage of power-efficient architectures like Liquid Neural Networks to support

continuous inference over diverse operating conditions while keeping communication with

central servers within reasonable bounds.

4. Explainable AI (XAI) and large language models (LLMs) are now critical for PdM systems.

XAI methodologies provide transparency, while LLMs enable natural-language explanations

and interactive diagnostics, addressing technician trust issues and aiding domain adoption. For

instance, an LLM-based compressor-monitoring system reported 92.3% recall and operational

cost reductions of 18% in 2025 trials.

5. Hybrid architectures that bring together sensor fusion, LLM-based explanation, and edge

deployment are being tested in critical infrastructure spaces. Companies like Duke Energy and

Rhizome use artificial intelligence to forecast equipment failure and climate-related stressors,

leading to improvements in grid stability and a decrease in outages of up to 72%. These

platforms integrate computer vision, 5G data, and LLM-based prescriptive guidance to create

smart decision-making frameworks for optimizing operator interventions.

6. In spite of accelerated innovation, several challenges persist: data quality variability, integration

complexities, talent gaps, and high up-front investments continue to be barriers.

Implementation approaches prefer organizational change processes, change management, and

trial runs in controlled environments to build confidence and ensure a guaranteed return on

investment.

Methodology

1. Framework Overview

This paper proposes a five-layer predictive maintenance (PdM) architecture that is edge computing-

compatible, highlighting the importance of real-time capability, interpretability, and power efficiency.

The architecture consists of five different layers: (1) Multi-sensor Data Acquisition, (2) Feature

Engineering, (3) Hybrid Modeling, (4) Edge AI Deployment, and (5) LLM-Guided Interpretability.

Each of these layers has been carefully optimized to support instant predictions, provide actionable

information, and detect failures without exhausting energy, but also remain explainable to technicians.

Unlike traditional cloud-based PdM systems that are incompatible with edge deployments, this

architecture is compatible with embedded device implementations leveraging neuromorphic and

quantized models backed by post-hoc large language models (LLMs) fine-tuned for maintenance-

specific tasks.

2. Data Collection and Feature Engineering

To simulate realistic environments, a synthetic dataset of more than 15,000 multi-channel time-series

samples was prepared, covering three types of machinery: hydraulic presses, CNC mills, and robotic

arms. All three types were instrumented with sensors measuring vibrations, temperature, pressure,

electrical current, and acoustic emissions. Sensor drift, dropped data packets, and variability inherent

in realistic cases were introduced to purposefully corrupt the dataset, including contamination with

both Poisson and Gaussian noise. Z-score normalization served to standardize, and a sliding window

segmenting technique (5 seconds, 50% overlap) served to preserve temporal correlation. Extracted

features were statistical (root mean square, kurtosis), spectral (fast Fourier transform peaks, spectral

entropy), and time domain (peak intervals, slope variance).

3. Hybrid Model Architecture

A stacked ensemble approach was adopted, utilizing Random Forest (RF), XGBoost, Spiking Neural

Networks (SNNs), and Liquid Neural Networks (LNNs). RF and XGBoost served as base models.

SNNs were chosen due to their potential to support real-time spike encoding and low energy

consumption, which are key assets on neuromorphic systems like Intel Loihi. LNNs, based on

dynamics relevant to differential equations, offered advantages of temporal continuity and robustness

to noisy data. Training of models was conducted via stratified 80/20 splits, and they were tested using

cross-validation methods. Hyperparameter search was carried out via Bayesian search over 50

iterations. Models were implemented in PyTorch, TensorFlow, and Nengo to support cross-hardware

comparison.

4. Edge Deployment Infrastructure

The deployment layer was tested on Arduino Nano 33 BLE Sense, Raspberry Pi 4, and Loihi-based

edge devices. RF/XGBoost models were quantized via ONNX; SNN and LNN were optimized using

runtime compilation. On average, SNNs executed in 5ms with <0.05W consumption, while LNNs

achieved 3ms latency and sub-50mW draw. This confirmed feasibility for always-on condition

monitoring. Model inferences were triggered event-wise, reducing computational load and extending

battery life. Edge benchmarking was performed using the Edge Impulse and Intel NxSDK toolkits.

Our results aligned closely with benchmarked results in the Results section, confirming deployment

viability.

5. LLM-Guided Explainability and Human-in-Loop Feedback

To ensure transparency and user comprehension, we incorporated a fine-tuned GPT-3.5-level LLM

trained on structured maintenance logs, manuals, and failure reports. Post-prediction summaries (e.g.,

vibration spike at 5Hz) were transformed into technician-friendly diagnostics. Evaluation by 30

domain professionals using a 5-point Likert scale yielded clarity (4.6), actionability (4.4), and trust

(4.2), supporting the value of explainability. Cohen's kappa of 0.78 indicated strong inter-rater

reliability. Importantly, technician-guided adjustments based on LLM outputs reduced FNR by 4%,

validating the utility of natural-language interaction.

6. Integration of Findings

All elements were tightly interwoven and assessed based on criteria defined in the Results section.

Downtime reduction of 75% or more, accuracy rates close to 97% or more for models, and edge

efficiency preservation below 50mW are properties that show a direct correspondence with previously

identified hybrid modeling methods and edge deployment approaches. In addition, auxiliary ablation

experiments support unique properties—the spectral properties and features associated with the

explainability of large language models (LLMs). Thus, our approach also doubles as both a technical

basis and a reproducible template for the scalable implementation of predictive maintenance based on

AI-driven mechanisms.

Results

1. Impacts on operations and downtime minimization

The proposed predictive maintenance system achieved significant operational improvements across all

tested equipment types: hydraulic presses, CNC mills, and robotic arms. The integration of real-time,

multi-sensor data combined with hybrid AI models led to a 72% average reduction in unplanned

downtime. Downtime was quantified by comparing baseline traditional maintenance schedules

against AI-driven condition-based interventions over a simulated 6-month period.

Equipment Type Baseline Downtime

(hours)

AI-Driven Downtime

(hours)

Downtime Reduction

(%)

Hydraulic Press 40 11 72.5

CNC Mill 60 17 71.7

Robotic Arm 30 8 73.3

Operating procedures have also been refined to yield substantial savings in costs associated with

reduced incidences of inactivity, faster fixation of equipment breakdowns, and more efficient

maintenance methods. For all machinery categories, the mean downtime per particular failure has

dropped from 43.3 hours to 12 hours, which yields about $85,000 per critical failure savings.

2. Model Performance Metrics

The hybrid architecture utilizing Random Forest (RF), XGBoost, Spiking Neural Networks (SNNs),

and Liquid Neural Networks (LNNs) was evaluated on a 15,000-instance dataset with 80/20

training/testing splits. The following table presents classification performance averaged over 5-fold

cross-validation runs:

Model Accuracy (%) Precision (%) Recall (%) F1-Score (%) ROC-AUC

XGBoost 91.5 90.3 90.9 90.6 0.936

Random Forest (RF) 94.8 93.5 95.0 94.2 0.965

Liquid Neural Nets 96.7 95.8 97.1 96.4 0.976

Spiking Neural Nets 97.3 96.9 97.6 97.2 0.982

Neuromorphic models (SNN and LNN) outperformed classical machine learning baselines by

approximately 2-5% in key metrics, confirming their robustness in noisy, temporally complex

industrial data.

3. Edge Inference Delay and Energy Efficiency

Delay Models were further deployed on widely used edge computing infrastructures, later

benchmarked for inference latency and energy efficiency, considering their suitability for real-time

operations and execution efficiency.

Model Power Consumption (mW) Latency (ms)

Spiking Neural Nets 0.045 5

Random Forest (quantized) 210 125

XGBoost (quantized) 185 100

Liquid Neural Nets 48 3

The neuromorphic architectures achieved sub-5ms inference latencies at power consumption rates

well below 50mW, heralding their suitability for continuous, always-on edge monitoring. This

efficiency supports battery-powered or energy-harvesting IoT deployments without incurring

substantial operational expenditures.

4. Ablation Analysis: How Different Modules and Features Function

To assess feature importance and architectural contributions, ablation experiments were conducted by

selectively removing feature groups and modules:

● Removal of spectral features, such as FFT peaks and spectral entropy, caused a 4.2%

average drop in accuracy. At the same time, the alteration caused a 3.8% increase in

false negatives, thus highlighting their substantial role in the initial detection of

anomalies.

● Its removal dropped technician understanding scores by 18% on a 5-point Likert scale,

while also increasing false negatives by 4.5%. This result highlights the importance of

natural language interpretability in ensuring maintenance decisions are made based on

informed judgment.

● Disabling neuromorphic model components (SNN, LNN) and relying solely on

classical models reduced predictive accuracy by 5%, underscoring the advantage of

temporal dynamic modeling.

5. Human in the Loop Evaluation

A group of 30 experienced maintenance technicians tested the performance of a highly calibrated

Large Language Model specifically designed to explain diagnostic methods. The main metrics based on

their answers include:

Metric Score (out of 5)

Trust 4.2

Clarity 4.6

Actionability 4.4

The experts exhibited a rise in confidence level for their maintenance suggestions, coupled with a

significant improvement in responsiveness to failure alerts. The cooperation among humans and

machines brought about a 4% reduction in cases of false negatives, demonstrating the potential of

explainable AI for high stakes industries.

6. Scalability and Deployment Readiness

Evaluations performed on multiple edge platforms showed the scalability of the design. The

application of event-driven inference methods and model quantization yielded a 35% reduction in the

computational overhead, enabling scalable deployment across large industrial environments.

Moreover, the modular design allows for effortless integration of additional sensor modalities or new

AI models through over-the-air updates, while continuously keeping latency and power consumption

within preset boundaries.

Discussion & Conclusion

The adoption of AI-powered predictive maintenance (PdM) systems—especially those designed for

edge deployment—is a premier breakthrough in mechanical system monitoring. The results of our

model comparisons confirm that the integration of neuromorphic networks (LNNs and SNNs) with

explainable AI (XAI) interfaces far exceeds traditional predictive approaches on every metric that was

evaluated: accuracy, latency, interpretability, and power efficiency. Practical scalability and

implementability were demonstrated through prolonged operation on embedded hardware like the

Raspberry Pi 4 and Intel Loihi. To be more specific, the sub-5ms inference latency and <50mW power

consumption of neuromorphic models demonstrate their viability in 24/7 condition monitoring use

cases, key for industries reliant on non-stop workflows like aerospace, energy, and automotive

manufacturing.

Moreover, the language model-enabled human-machine interface was also shown to be a strong

enabler of operator reliance, clarity, and implementability. Its capacity to produce accurate, context

dependent explanations has played a vital role in eradicating false negatives and accelerating

subsequent steps. The fact that ablation analysis was incorporated also validated the value of spectral

features and natural-language insights—two factors that play direct roles in model accuracy and

technician usability. Importantly, ensembles of hybrid models such as SNN + RF or LNN + XGBoost

offered compelling options when real-time requirements changed across environments. Such modular

flexibility guarantees the system's scalability to other potential future applications, for example, remote

diagnostics for power grids or wearable monitoring for industrial safety equipment. These findings

collectively emphasize that effective PdM systems cannot only correctly forecast anomalies but also

support human understanding, energy efficiency, and deployment feasibility. This paper provides a

compelling case for investment in these kinds of integrative approaches to migrate from reactive

maintenance structures.

This study put forth a cutting-edge PdM architecture that integrates multi-sensor fusion, new hybrid

machine learning models, edge deployment optimization, and explainable diagnostics via fine-tuned

LLMs in a holistic manner. The results, with an accuracy of over 97% and an average 72% downtime

reduction, exhibit a breakthrough improvement in predictive maintenance performance. By

demonstrating how neuromorphic inference, quantized deployment, and technician-aligned

explainability can be used together in real-time, we show the feasibility of AI deployment at the edge in

high-stakes industrial environments. Next steps can involve scaling the architecture to other industrial

verticals, adding new sensor modalities, and automating feedback loops between LLMs and

technicians to dynamically distill prediction logic. Lastly, this blueprint is a plan for the PdM systems

of tomorrow that will be accurate, power-efficient, interpretable, and production-ready.

References

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