The research team, led by Zhanpeng Liu and Wensheng Xiao, combined failure mode and effects analysis, Bayesian networks, risk matrices, and fuzzy comprehensive evaluation into one integrated method. Each technique has well-known strengths and blind spots. FMEA can systematically identify potential failure modes but is highly subjective. Bayesian networks excel at quantitative forward and backward inference yet cannot judge how severe a failure’s consequences would be. Risk matrices offer a visual representation of occurrence and severity but cannot assess the risk level of the system as a whole. Fuzzy comprehensive evaluation handles vague, uncertain information but again depends on subjective judgment. By weaving these approaches together, the researchers aimed to let each method compensate for the weaknesses of the others, producing a single assessment that yields both the system failure probability and the risk level of every individual failure mode.

The workflow begins with a fault tree analysis of the motor, in which twenty bottom-event failure modes across six components, the rotor, bearings, windings, permanent magnets, controller, and mechanical seal, are mapped into a Bayesian network. Prior probabilities are derived from historical failure rates in the OREDA offshore reliability database, adjusted upward by expert judgment to reflect the brutal conditions of underground drilling. Bayesian forward inference then computes the overall system failure probability, while backward diagnostic inference produces posterior probabilities revealing which failure modes are most likely responsible when the system fails. In the baseline assessment, the system failure probability came out at 0.428, a strikingly high figure that underscores how punishing the drilling environment is for these machines.

Next comes importance analysis, a mathematical step that quantifies how much each failure mode contributes to system failure. The team calculated three indices: probabilistic importance, which measures the impact of a component’s failure probability on system failure; structural importance, which reflects a component’s position in the system architecture; and critical importance, which combines both failure probability and sensitivity. Because all twenty failure modes feed the system through identical OR-gate logic, their structural importance is equal, but their probabilistic and critical importance diverge dramatically. Rotor breakage, for instance, posted a probabilistic importance of 1.3782, far above its peers, while bearing wear registered a posterior probability of 0.612, the highest of any mode, simply because bearings grind against each other every moment the motor spins.

These quantitative outputs then feed the qualitative side of the framework. The posterior probability of each failure mode determines its occurrence rating on a five-level scale, from improbable to frequent, while the critical importance determines its severity rating, from negligible to catastrophic. The two ratings fuse into a composite risk index called ROS, weighted 0.4 toward occurrence and 0.6 toward severity, a deliberate choice reflecting the safety-critical nature of drilling, where even rare events with devastating consequences demand priority attention. The resulting ROS values map onto a fuzzy evaluation matrix through a parabolic membership function, and the weights of that matrix come directly from the importance analysis rather than from pure expert opinion, reducing the subjectivity that plagues conventional assessments.

The baseline results identified winding phase-to-phase short circuit as the single highest-risk failure mode, with a ROS value of 4.2 placing it firmly in the high-risk category. Rotor dynamic eccentricity and permanent magnet partial demagnetization followed at 3.6 each, classed as medium-high risk. Perhaps more revealing were the failure modes with severe consequences but vanishingly low occurrence, including rotor breakage, bearing breakage, and permanent magnet breakage, all rated medium risk despite being improbable. The framework’s dual weighting captured exactly the kind of nuance that pure probability-based methods miss: a rare failure that destroys the motor deserves as much managerial attention as a common one that merely degrades performance. The fuzzy evaluation concluded that the system sat at medium risk with a membership probability of 0.498, but with a worrying 0.351 probability of medium-high risk.

Armed with this diagnosis, the researchers proposed targeted countermeasures for every failure mode, ranging from corona-resistant enameled wire and vacuum pressure impregnation for the windings to hastelloy springs and fluorocarbon or EPDM elastomers for the seals, matched to oil-based and water-based mud chemistries respectively. For winding faults, they applied weak magnetic current injection, multi-phase redundancy, and winding redundancy, allowing the motor to shed faulty phases gracefully while maintaining partial power. After implementing the improvements, the system failure probability dropped from 0.428 to 0.394, the mean time between failures rose from 1,847 to 2,032 hours, and the medium-high risk membership probability fell from 0.351 to 0.245. The highest-risk failure mode, the winding short circuit, saw its ROS value plunge from 4.2 to 3.2, a 23.8 percent risk reduction.

What distinguishes this study from earlier hybrid approaches is its unusually rigorous treatment of uncertainty and validation. The team ran a Monte Carlo simulation with 10,000 samples, assuming failure rates follow log-normal distributions with a 20 percent coefficient of variation, and found that the 95 percent confidence intervals of key outputs remained narrow, less than 15 percent of their mean values. A perturbation analysis of the weighting coefficients showed that risk rankings stay stable, with Spearman rank correlations above 0.88, as long as severity retains reasonable dominance, confirming the wisdom of the 0.4-0.6 split. The importance-based weights were cross-checked against the entropy weight method and the CRITIC method, with Kendall’s coefficient of concordance reaching 0.82, indicating strong agreement between expert judgment and data-driven weighting. A paired bootstrap test and a Wilcoxon signed-rank test both confirmed that the post-improvement risk reduction was statistically significant, with p below 0.001.

The authors also introduced three methodological innovations that broaden the framework’s reach. A risk coupling coefficient quantifies how strongly each failure mode couples with environmental loads such as vibration, high temperature, current fluctuations, and high-pressure drilling fluid, distilled from a multi-factor coupling matrix that identified twenty high-probability coupled failure modes. An adaptive risk transfer function models nonlinear propagation from component-level risk to system-level risk, capturing synergistic effects that simple OR-gate logic overlooks. And a risk entropy metric measures how dispersed or concentrated the system’s risk profile is; it fell from 1.42 before improvement to 1.24 after, signaling reduced uncertainty in the risk landscape.

The framework is not without limitations, which the authors acknowledge candidly. Integrating four methodologies creates a complex model with substantial computational overhead, and exact Bayesian inference scales exponentially with the number of failure modes, though hierarchical decomposition into sub-networks reduces the complexity from an intractable O(2^100) to a manageable O(10 times 2^10) for a hundred-mode system split across ten components. Prior probabilities still lean on the OREDA database and expert experience, which update infrequently, and the static Bayesian network does not yet ingest real-time sensor data. Future work, the team suggests, will move toward dynamic Bayesian networks fed by live monitoring evidence, adaptive neuro-fuzzy membership functions optimized by backpropagation, and hierarchical architectures spanning failure mode, component, subsystem, and system layers. For now, the study offers drilling engineers something genuinely valuable: a single, statistically validated assessment that tells them not just whether their motor is likely to fail, but exactly which of twenty failure modes deserves their attention first, and how much safer each repair will make it.

Subject of Research: Hybrid fuzzy risk assessment of drilling permanent magnet synchronous motors using FMEA and Bayesian networks

Article Title: An improved fuzzy risk assessment method fusing FMEA and BN for drilling permanent magnet synchronous motors

Article References: Liu, Z., Xiao, W., Cui, J., Wang, H., & Mei, L. (2026). An improved fuzzy risk assessment method fusing FMEA and BN for drilling permanent magnet synchronous motors. Results in Engineering, 32, Article 113047. https://doi.org/10.1016/j.rineng.2026.113047

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113047

Keywords: risk assessment, FMEA, Bayesian network, fuzzy comprehensive evaluation, permanent magnet synchronous motor, drilling motor, reliability engineering, risk matrix, Monte Carlo simulation, importance analysis, oil and gas drilling, failure modes

The research, led by Hima Soni of Gujarat Technological University together with colleagues at Vishwakarma Government Engineering College, Tata Consultancy Services, MultiQoS and MDI Gurgaon, introduces a framework that combines variational autoencoders with actor-critic deep reinforcement learning to estimate remaining useful life sequentially, refining its estimate step by step rather than producing a single one-shot prediction. The result, according to the authors, is a method that outperforms conventional supervised sequence-learning baselines, including long short-term memory networks and multilayer perceptrons, by 26.7 to 40.3 percent in root mean square error, with the best-performing variant reaching an RMSE of 0.1262.

To understand why this is notable, it helps to unpack the machinery underneath. Remaining useful life estimation is fundamentally a problem of reading degradation. Industrial machines are fitted with sensors that stream measurements of temperature, vibration, pressure and dozens of other signals. As components wear, these signals drift in characteristic ways, and the art of prognostics lies in mapping those drifts onto a timeline of future failure. Classical approaches feed sensor histories into supervised models that learn the mapping directly from labeled run-to-failure data. The trouble is that such data is scarce, noisy and expensive to collect, and the models often struggle to generalize across different degradation patterns.

The new framework attacks the problem in two stages. First, a variational autoencoder compresses the raw sensor streams into a compact latent representation. A variational autoencoder is a generative neural network that learns to encode data into a probabilistic latent space and then decode it back, forcing the latent variables to capture the essential structure of the data. In this application, the latent space preserves the underlying degradation dynamics while discarding redundant or irrelevant sensor noise. Crucially, the trained autoencoder then serves as a surrogate environment, a simulated world in which a reinforcement learning agent can act without touching real machinery or requiring vast amounts of fresh field data.

Inside that surrogate environment, the researchers deployed two actor-critic deep reinforcement learning algorithms: Twin Delayed Deep Deterministic Policy Gradient, known as TD3, and Proximal Policy Optimization, known as PPO. Both algorithms learn a policy, a rule for choosing actions, while simultaneously learning a value function that estimates how good those actions are. The agent’s task is to iteratively refine degradation trajectories within the learned latent space, adjusting its estimates of remaining useful life in a sequence of decisions rather than a single leap. This sequential formulation turns prognostics into a control problem, allowing the model to correct course as new evidence accumulates.

One of the study’s most interesting findings concerns reward shaping, the practice of designing the feedback signal that guides reinforcement learning. The researchers compared multiple reward formulations, including a weighted cosine similarity reward that measures how closely the agent’s predicted trajectory aligns with the true degradation path. Their comparative evaluation showed that the choice of optimization strategy meaningfully shapes both learning stability and downstream prediction accuracy. The on-policy approach, PPO, yielded more consistent convergence in 11 of 12 experimental configurations, a result that speaks to the well-known stability advantages of on-policy methods, which evaluate and update the same policy they use to collect experience. Meanwhile, the weighted cosine similarity reward produced comparable prediction accuracy, with large Cohen’s d effect sizes exceeding 0.8 in 10 configurations, indicating that the reward design produced differences large enough to matter in practice, not merely statistical noise.

The performance numbers are striking. An RMSE of 0.1262 on remaining useful life estimation, achieved while beating LSTM and MLP baselines by margins approaching 40 percent, suggests that the latent-space policy optimization is doing genuine work rather than simply repackaging existing sequence-learning tricks. The authors attribute this to the synergy between generative representation learning and sequential policy optimization: the autoencoder gives the agent a clean, information-dense state space, while the reinforcement learning loop gives the model a mechanism for iterative refinement that supervised models lack. Because the agent learns within a surrogate environment, the architecture is also computationally scalable and data efficient, qualities that matter enormously in industrial settings where labeled failure data is the bottleneck.

The broader context makes the contribution clearer. Predictive maintenance has accumulated a rich toolkit over the past decade, from deep convolutional neural networks for prognostics to Bayesian neural networks that quantify uncertainty, from regularized LSTM models for rolling bearings to ensemble deep learning with multi-objective optimization for rotating machinery. Reinforcement learning has already appeared in maintenance research, notably in scheduling and optimization roles, such as deep reinforcement learning for predictive aircraft maintenance using probabilistic prognostics. What distinguishes the new work is its modular marriage of a generative surrogate environment with policy optimization directly in latent space, a design the authors argue is adaptable to a variety of degradation patterns and therefore suited to challenging industrial scenarios where one-size-fits-all models falter.

There are, of course, caveats worth keeping in mind. The study reports that no datasets were generated or analysed during the current study in its data availability statement, and the evaluation rests on the configurations the authors describe, so practitioners will want to validate the approach on their own fleets before deploying it. Reinforcement learning remains notoriously sensitive to hyperparameters and reward design, and although the comparative evaluation across 12 configurations is a step toward taming that sensitivity, the finding that PPO converges more consistently than TD3 in most settings is itself a reminder that algorithm choice is not a detail but a first-order design decision. The authors also emphasize that the framework is modular, which means each component, the autoencoder, the policy optimizer and the reward function, can in principle be swapped as better versions emerge.

Still, the direction is compelling. If machines could reliably tell operators how much life they have left, maintenance could shift from reactive scrambling and wasteful over-servicing toward precisely timed intervention, saving money and preventing failures before they happen. By reframing remaining useful life estimation as sequential policy optimization in a learned latent space, this work offers a template for how generative models and reinforcement learning might together make that vision practical. The authors thank Gujarat Technological University and Bacancy DataProphets Private Limited for supporting the research, and their results suggest that the intersection of representation learning, reward design and control may be one of the more productive frontiers in reliability engineering for years to come.

Subject of Research: A VAE-based deep reinforcement learning framework with reward shaping for sequential estimation of machinery remaining useful life.

Article Title: Sequential Estimation of Remaining Useful Life Through VAE-Based Deep Reinforcement Learning With Reward Shaping

Article References: Sequential Estimation of Remaining Useful Life Through VAE-Based Deep Reinforcement Learning With Reward Shaping. (n.d.). https://doi.org/10.1007/s10994-026-07152-5

Image Credits: AI Generated

DOI: 10.1007/s10994-026-07152-5

Keywords: predictive maintenance, remaining useful life, deep reinforcement learning, variational autoencoder, reward shaping, TD3, PPO, prognostics, latent space, reliability engineering, machine learning, sensor data

The problem the researchers set out to solve is one that lurks inside nearly every eco-design effort. When a manufacturer wants to extend a product’s useful life, the temptation is to modify the part most responsible for wear and tear, and to consider that part in isolation. But products are not collections of independent components; they are webs of coupled functional chains in which one part’s behavior constrains another’s. The study shows that many lifespan-extension practices overlook this interdependence, along with the environmental impacts that design changes themselves introduce. The result is that engineers often cannot identify which parts are truly critical to both longevity and environmental performance, and cannot guarantee that a longer-lived product actually delivers net sustainability benefits rather than simply shifting burdens elsewhere in the life cycle.

To close this gap, the team built a framework that links part-level design changes directly to three outcomes: lifespan, environmental impact, and cost. The approach begins by mapping how components depend on one another functionally, borrowing concepts from design structure matrix methods that have long been used in systems engineering to untangle complex products. Reliability engineering then enters the picture through Weibull-based lifetime modeling, a statistical approach widely used to predict how long a component will survive under given operating conditions. By combining coupling analysis with lifetime modeling, the framework can identify which components must be redesigned together, and how changes to one ripple through the durability of the whole.

The air circuit breaker served as a rigorous test bed. These devices protect electrical circuits in industrial settings, and their lifespan is typically measured not in years of service but in switching cycles, the number of times they can open and close safely. Two components emerged as central: the contact tip, which endures the physical stress of each switching operation, and the rear connection, which anchors the assembly and is vulnerable to the mechanical and thermal conditions created by the moving contact. The analysis found that these two parts are strongly coupled. In practical terms, any effort to thicken or toughen the contact tip must be coordinated with the choice of materials for the rear connection, because the two components interact in ways that jointly determine whether the breaker meets its lifespan target and its environmental requirements.

The team then applied the framework to a concrete design target: a breaker capable of enduring 6,000 switching cycles at 90 percent reliability. Using their coupled analysis, they identified a feasible design space before any optimization took place: a contact tip thickness range of roughly 2.0 to 2.5 millimeters. This step matters because it treats lifespan as a prior constraint rather than as one objective to be traded away later. Within that lifespan-secured space, the remaining decision was which material to use for the rear connection, and here the framework exposed a genuine trade-off between carbon and cost. Choosing copper delivered the larger environmental payoff, cutting the carbon emissions associated with the equipment by 34 to 36 percent, while recycled aluminum limited any increase in production cost to less than 2 percent but offered a smaller carbon benefit. Neither choice dominates; the framework’s contribution is making the trade-off visible and quantifiable at the design stage, when decisions are still cheap to change.

Perhaps the most counterintuitive finding concerns reliability itself. Designers often assume that demanding higher reliability is unambiguously good, since it means fewer failures in the field. But the study shows that reliability requirements and product lifespan interact in complex ways. When the researchers raised the reliability level from 78 to 90 percent, the projected lifespan fell by 15 percent while carbon emissions rose by 1.9 percent. The mechanism is subtle: requiring a component to survive under stricter statistical guarantees changes the way lifetime must be modeled and can push designs toward configurations that are, in aggregate, shorter-lived and more emissions-intensive. The implication is that environmental impact assessments of electronic equipment cannot treat lifespan as a fixed number; they must always state the reliability level that lifespan corresponds to, otherwise comparisons between designs are meaningless.

These results arrive at a moment when the policy landscape is shifting rapidly. The European Union’s Ecodesign for Sustainable Products Regulation, which entered into force in 2024, pushes manufacturers to design for durability, repairability, and circularity, while international standards such as IEC 62271 for switchgear and NF EN 45552 for assessing durability of energy-related products increasingly require quantitative methods for product longevity. Meanwhile, the Global E-waste Monitor 2024 documented that only a fraction of the world’s discarded electronics is formally collected and recycled, leaving vast quantities of embodied materials and emissions to leak into landfills and informal waste streams. Tools that let designers see, early on, how lifespan, emissions, and cost interact could therefore shape products far more effectively than end-of-pipe recycling alone.

The study distills its findings into three guiding principles. First, eco-design should evaluate coupled functional chains rather than isolated parts, because the components that govern longevity frequently interact and cannot be optimized one at a time. Second, lifespan should be established as a prior design constraint before multi-objective optimization begins; if durability is merely one objective among many, optimization algorithms may sacrifice it in favor of cost or weight, quietly undermining the circularity goal. Third, environmental impact assessment should consider product lifespan together with the reliability level at which that lifespan is defined, ensuring that reported lifetimes are statistically meaningful and comparable across designs. Taken together, these principles shift eco-design from a part-by-part checklist exercise toward a systems view of product sustainability.

The framework’s significance extends beyond circuit breakers. Electrical and electronic equipment spans everything from consumer devices to grid infrastructure, and the authors’ earlier work, including a knowledge-graph-based method for green design evaluation of such equipment published in ACS Sustainable Chemistry & Engineering in 2023, has been building toward exactly this kind of integrated, quantitative approach. The new study, funded by the Fujian Provincial Science and Technology Department, the Chinese Academy of Sciences, and the National Natural Science Foundation of China, demonstrates the method on real industrial data provided by a partner company under confidentiality, lending it practical credibility. As regulators tighten durability requirements and manufacturers confront both climate targets and cost pressures, the message of this research is likely to resonate: the greenest product is not simply the one built with the cleanest material or the thickest part, but the one whose designers understood how every component’s fate is bound to every other’s, and who defined, from the start, how long the product should last and with what statistical confidence.

Subject of Research: Eco-design and environmental impact assessment of electrical and electronic equipment incorporating product lifespan and component interdependencies

Article Title: Incorporating product lifespan into eco-design and environmental impact assessment of electrical and electronic equipment

Article References: Liu, G.-G., Wang, Q.-C., Fan, L., Wang, J.-Y., & Chen, W.-Q. (2026). Incorporating product lifespan into eco-design and environmental impact assessment of electrical and electronic equipment. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00181-2

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00181-2

Keywords: eco-design, electronic waste, product lifespan, life cycle assessment, reliability engineering, Weibull model, air circuit breaker, circular economy, carbon emissions, multi-objective optimization, electrical equipment, sustainability