An intelligent tool for monitoring and managing the condition of equipment and machinery that collects operational data and potential errors from various sources and displays them in the form of understandable indicators and graphs. This dashboard allows for real-time monitoring of system health, prediction of failures
- State Estimation & Monitoring Algorithms Markov Models (DTMC, CTMC, Semi-Markov) – For probabilistic state transitions. Hidden Markov Models (HMMs) – Estimating hidden system states from observations. Kalman Filters / Extended Kalman Filters – For continuous-state estimation. Particle Filters – Nonlinear/non-Gaussian state estimation. Observer-Based Methods (Luenberger Observer) – For reconstructing internal states.
- Control & Maintenance Algorithms Feedback Control (PID, Adaptive Control) – Keeps system stable across states. Model Predictive Control (MPC) – Optimizes control actions considering future states. Reinforcement Learning (Q-Learning, Policy Gradient, Deep RL) – Learns optimal actions for maintaining performance in multi-state environments. Fault Detection & Diagnosis (FDD) Algorithms – Identifies and mitigates faults to maintain system states.
- Optimization & Scheduling Algorithms Dynamic Programming (DP) – Optimal policies for state-dependent decisions. Markov Decision Processes (MDPs) – Decision-making under uncertainty. Partially Observable MDPs (POMDPs) – When states are not fully observable. Genetic Algorithms / Evolutionary Algorithms – Optimizing maintenance schedules. Integer/Linear Programming – For multi-state resource allocation and scheduling.
- Reliability & Maintenance-Specific Algorithms Multi-State Reliability Models (MSR) – For systems with performance degradation (not just "working" or "failed"). Preventive Maintenance Optimization (PMO) Algorithms – Scheduling based on degradation levels. Condition-Based Maintenance (CBM) Algorithms – Data-driven maintenance decisions. Proportional Hazards Models (PHM) – Reliability modeling across states. Monte Carlo Simulation – Evaluating system reliability under multi-state behavior.
- Learning & Data-Driven Algorithms Clustering & State Classification (K-Means, Gaussian Mixtures, HDBSCAN) – Identifying system states from data. Neural Network State Predictors – Predicting future system states. Hybrid Physics + Machine Learning Models – For complex, nonlinear multi-state systems. Bayesian Networks – Probabilistic reasoning about multiple interdependent states.
- Specialized Algorithms
Petri Nets – Modeling and analyzing multi-state discrete-event systems.
Finite State Machines (FSMs) – Rule-based state transitions.
Stochastic Petri Nets (SPN) – For probabilistic, timed, and concurrent states.
Agent-Based Models – Decentralized multi-state system maintenance.
Resilience Optimization Algorithms – Ensuring recovery after disruptions.
