OMIn Dataset: Operations & Maintenance Intelligence

• OMIn datasets are comprehensive collections of high-dimensional sensor streams, maintenance records, and asset metadata used for predictive and prescriptive maintenance.
• They employ advanced analytics including tensor decomposition, sequence mining, and statistical process control to extract actionable insights and enhance asset reliability.
• Integration of expert knowledge with data-driven models enables effective maintenance scheduling, operational cost reduction, and automation in critical industrial applications.

Operations and Maintenance Intelligence (OMIn) Dataset refers to a class of data resources and associated methodologies that enable comprehensive, data-driven analysis, modeling, and optimization of industrial and municipal asset management, with a central focus on predictive and prescriptive maintenance. OMIn datasets typically integrate high-dimensional, heterogeneous data from physical asset telemetry, maintenance logs, operational schedules, and contextual metadata, thereby facilitating actionable insights into system-wide reliability, operational costs, and maintenance decision processes. The following sections detail the principal facets of OMIn datasets and their research ecosystem, synthesizing methodologies, technical formulations, and domain applications directly from recent literature.

1. Structure and Scope of OMIn Datasets

OMIn datasets are characterized by their complex, multivariate, and temporally indexed structure, often combining:

• Sensor streams: Time series measurements such as vibration, temperature, pressure, and SCADA (Supervisory Control And Data Acquisition) data (e.g., wind turbine, power grid, vehicle fleet).
• Maintenance records: Structured or unstructured logs detailing failure events, system diagnostics, replacement/repair actions, and time-to-event annotations.
• Asset metadata: Make/model, specification, and usage context (e.g., load, duty cycle, environmental exposure).
• Operational context: Schedules, crew assignments, production rates, and asset deployment patterns.

For example, municipal vehicle fleet maintenance datasets are aggregated as three-dimensional tensors with axes corresponding to vehicles, systems (components), and time (Gardner et al., 2017). Power grid OMIn datasets track equipment condition attributes, annual inspection health indices, and maintenance interventions across utility assets (Martin et al., 2020).

Data attributes and collection schemas are designed for interoperability, reuse, and the enablement of advanced analytics. The integration of raw sensor data, expert-filled records, and asset inventory supports both descriptive statistics and automated predictive modeling.

2. Advanced Analytical and Modeling Techniques

OMIn research leverages diverse computational methodologies:

A. Tensor Decomposition and Multimodal Analytics

• PARAFAC Decomposition: Fleet maintenance actions are represented as three-way tensors (vehicles × systems × time), decomposed via PARAFAC as

$$\mathcal{X} \approx \sum_{r=1}^R \mathbf{a}_r \circ \mathbf{b}_r \circ \mathbf{c}_r$$

where the rank-one factors reveal maintenance patterns across asset populations, component types, and temporal indices (Gardner et al., 2017).

• Multimodal Data Fusion: Integration of SCADA time series, natural language maintenance actions, and imagery within knowledge graph frameworks supports cross-linked analytics and explainable O&M decision support (Chatterjee et al., 2020, Ai et al., 7 Oct 2025).

B. Sequential and Pattern Mining

• Differential Sequence Mining: Statistical mining of maintenance event subsequences (of length ≥3) distinguishes unique, repeated activity chains for particular make/model classes. Statistical significance is established via difference-of-proportions z-tests on normalized support metrics (Gardner et al., 2017).
• Event Sequence Prediction: LSTM-based models are trained to forecast the next maintenance event from observed historical repair sequences; performance is evaluated using perplexity,

$$\text{Perplexity} = \exp\left(-\frac{1}{N} \sum_{i=1}^N \ln p_{\text{target},i}\right)$$

yielding superior prediction accuracy over random choice, and operationalizing anticipatory scheduling (Gardner et al., 2017).

C. Statistical Process Control and Anomaly Detection

• Attribute Oriented Induction (AOI): Hierarchical clustering and generalization of sensor attributes, enhanced by expert-driven weighting and abstraction hierarchies, distill a quantification function for machine health. This quantification function is monitored using Exponentially Weighted Moving Average (EWMA) control charts:

$$\begin{align*} \text{UCL} &= \mu_0 + L \cdot \frac{\sigma}{\sqrt{n}} \cdot \sqrt{\frac{\lambda}{2-\lambda} \left[1-(1-\lambda)^{2i}\right]} \ \text{LCL} &= \mu_0 - L \cdot \frac{\sigma}{\sqrt{n}} \cdot \sqrt{\frac{\lambda}{2-\lambda} \left[1-(1-\lambda)^{2i}\right]} \end{align*}$$

with anomaly detection triggering downstream LSTM-based Remaining Useful Life (RUL) estimation (Fernandez-Anakabe et al., 2019).

D. Knowledge Graphs and Retrieval-Augmented Generation

• Knowledge Graph (KG) Construction: Entities (components, failure modes, maintenance actions) and semantic relations are extracted from OMIn record corpora and stored as weighted triplets

$$G = \{(h, t, r, w) \mid h, t \in V, r \in R, w \text{ is frequency}\}$$

supporting both dataset-wide global sensemaking and flexible retrieval for action-oriented question answering (Ai et al., 7 Oct 2025).

• KG-RAG Pipelines: Queries are matched to KG nodes using semantic similarity (cosine score on embeddings), expanded to multi-hop subgraphs, and context is provided to locally executed LLMs for secure QA and high-stakes reasoning. For instance, importance-aware MST expansion filters KG context for summarization (Ai et al., 7 Oct 2025).

3. Integration of Expert Knowledge and Domain Context

OMIn approaches commonly blend learned models with expert domain knowledge:

• Concept hierarchies and weights in AOI: Experts specify abstraction layers and attribute importance, ensuring that lower-level, contextually critical sensor readings retain higher explanatory power (Fernandez-Anakabe et al., 2019).
• Formal Concept Analysis (FCA): Fuzzy lattices from FCA underpin Actionable Knowledge Graphs, with contextual recommendations ranked by precision, recall, and F-measure:

$$\begin{align*} P_{i,j} &= \frac{|F \cap A_j|}{|A_j|} \ R_{i,j} &= \frac{|F \cap A_j|}{|F|} \ F_{i,j} &= \frac{2 \cdot P_{i,j} \cdot R_{i,j}}{P_{i,j} + R_{i,j}} \end{align*}$$

contextualizing support for in-field maintenance operations (Fenza et al., 2020).

• Domain-adaptive LLMs: Specialized fine-tuning approaches (e.g., LORA-KR loss) and hierarchical agent decomposition preserve core knowledge and tailor reasoning to domain-specific tasks in maintenance scheme generation (Tao et al., 7 Nov 2024).

4. Optimization Models for Maintenance and Operations Coupling

OMIn datasets are increasingly used to inform integrated optimization models that simultaneously schedule operations and maintenance actions:

• Stochastic Mixed-Integer Programming (MIP): Decision variables for preventive/corrective actions, crew assignment, spare parts, operational dispatch, and logistic constraints are encoded, with sensor-driven degradation models updating failure risk and cost functions in real time (Fallahi et al., 2020, Bakir et al., 2021).
• Decomposition Algorithms: Two-stage decomposition (L-shaped, Benders) efficiently handle the coupling between discrete maintenance schedules and high-dimensional operational scenarios, ensuring scalability in multi-microgrid or wind farm settings (Fallahi et al., 2020, Bakir et al., 2021).
• Multi-head Attention Integration with MIP: Recent frameworks (e.g., AttenCOpt) embed MIP structure—objective and constraints—directly within a transformer-based MHA neural network, enabling rapid, feasible schedule generation and transfer learning across O&M problem scales (Kazemian et al., 31 Oct 2024).
• Bayesian Decision Processes with Pooled Learning: Data pooling across assets enables shared learning of deterioration rates; high-dimensional MDPs are decomposed into low-dimensional subproblems, supporting optimal control-limit and order-up-to policies dependent on collective experience (Drent et al., 2023).

5. Benchmarking, Evaluation, and Impact on O&M Practice

The OMIn dataset paradigm has shaped both methodological benchmarking and operational outcomes:

• Cost-based and task-completeness metrics: Datasets such as SCANIA Component X leverage cost-sensitive evaluation,

$$\text{Total\_cost} = \text{Cost}_{n,m} \times \text{No\_instances}$$

reflecting the asymmetry of risk in false positives vs. false negatives, fundamental for critical maintenance contexts (Kharazian et al., 26 Jan 2024).

• Coverage of Use Cases: OMIn resources support broad applications—from regression and survival analysis (predicting RUL) to anomaly detection, sequence forecasting, real-time scheduling, and root cause diagnosis (Gardner et al., 2017, Fernandez-Anakabe et al., 2019, Chatterjee et al., 2020, Kharazian et al., 26 Jan 2024).
• Demonstrated Outcomes: Integrated sensor-driven maintenance policies, as realized in microgrid and wind farm applications, yield quantifiable gains: >85% reductions in corrective maintenance actions, major cost and downtime savings, and improved renewable penetration (Fallahi et al., 2020, Bakir et al., 2021).
• Deployment and Extensibility: Publicly released datasets (e.g., SCANIA Component X, MaintNet resources), agent-based testbeds (AssetOpsBench), and KG-augmented reasoning frameworks (KEO) catalyze reproducibility, comparative evaluation, and secure deployment in safety/reliability-critical contexts (Akhbardeh et al., 2020, Patel et al., 4 Jun 2025, Ai et al., 7 Oct 2025).

6. Challenges, Limitations, and Future Directions

While OMIn datasets underpin advances in predictive maintenance and integrated asset management, several substantive challenges persist:

• Data heterogeneity and quality: Missing data, imbalanced event distributions, and non-standard log formats require robust pre-processing (e.g., leveraging techniques such as SMOTE, LMedS filtering, and domain-curated abbreviations) (Chatterjee et al., 2022, Akhbardeh et al., 2020).
• Interpretability and explainability: The adoption of explainable AI tools—attention mechanisms, SHAP/LIME analyses, and graph-based global sensemaking—addresses growing demand for human-interpretable, auditable recommendations (Chatterjee et al., 2020, Chatterjee et al., 2022, Ai et al., 7 Oct 2025).
• Scaling and computational tractability: Advances in optimization decomposition, MHA-based surrogates, and agent-oriented automation frameworks address the computational demands of large-scale, multi-asset scheduling (Fallahi et al., 2020, Kazemian et al., 31 Oct 2024, Patel et al., 4 Jun 2025).
• Secure/high-stakes deployment: Integration of locally deployable LLMs with KG-augmented RAG, rigorous evaluation by models-as-judges, and structured context expansion reflect the priority of robustness and factual reliability in e.g. aviation safety scenarios (Ai et al., 7 Oct 2025).
• End-to-end automation: Unified agent-based systems (AssetOpsBench) demonstrate that the OMIn dataset model can underpin perception, reasoning, and control loops that automate and orchestrate asset management workflows across the industrial lifecycle (Patel et al., 4 Jun 2025).

Future research is focused on advancing multimodal integration (sensor, logbook, image, and procedural data), refining continuous learning and adaptation strategies, and scaling OMIn-driven solutions for fully autonomous, trustworthy industrial asset management across domains.

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Table: Principal Methodologies in OMIn Dataset Analytics

Methodology	Key Mathematical Formulation	Example Use Case
Tensor Decomposition	$$\mathcal{X} \approx \sum_{r=1}^R \mathbf{a}_r \circ \mathbf{b}_r \circ \mathbf{c}_r$$	Fleet-wide pattern mining (Gardner et al., 2017)
EWMA Anomaly Detection	$$\text{UCL} / \text{LCL} = \mu_0 \pm L \cdot \frac{\sigma}{\sqrt{n}} \cdot ...$$	Turbine health monitoring (Fernandez-Anakabe et al., 2019)
LSTM Sequence Modeling	$$\text{Perplexity} = \exp(-\frac{1}{N} \sum_i \ln p_{\text{target},i})$$	Maintenance event prediction (Gardner et al., 2017)
Knowledge Graph QA	$$S = \frac{\text{emb}(q) \cdot \text{emb}(v)}{\|\text{emb}(q)\| \cdot \|\text{emb}(v)\|}$$	Dataset-wide reasoning (Ai et al., 7 Oct 2025)

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Summary

OMIn datasets form the foundational infrastructure for operations and maintenance intelligence, integrating high-fidelity asset, sensor, and record data with advanced analytical, optimization, and reasoning methodologies. The domain encompasses a spectrum of technical approaches—tensor methods, statistical learning, optimization (classical and deep attention-based), and knowledge-centric LLM models—each leveraging the structure of OMIn data to deliver improved maintenance scheduling, risk mitigation, operational cost savings, and actionable insight for large-scale industrial, municipal, and infrastructure asset fleets. Ongoing developments emphasize the importance of multimodal integration, explainable secure AI, and automation via agent-based systems, underscoring the continued centrality of the OMIn dataset paradigm in modern industrial operations research.