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VR training equipped with meta-heuristic disassembly planning algorithms has been widely applied in pre-employment training in recent years. However, these algorithms are usually authored for specific sequences of a single product, and it remains a challenge to generalize them to maintenance training with unpredictable disassembly targets. As a promising method for settling dynamic and stochastic problems, deep reinforcement learning (DRL) provides a new insight to dynamically generate optimal sequences. This study introduces the deep Q-network (DQN), a successful DRL method, to fulfill adaptive disassembly sequence planning (DSP) for the VR maintenance training. Disassembly Petri net is established to describe the disassembly process, and then the DSP problem is defined as a Markov decision process that can be solved by DQN. Two neural networks are designed and updated asynchronously, and the training of DQN is further achieved by backpropagation of errors. Especially, we replace the long-term return in DQN with the fitness function of the genetic algorithm to avoid dependence on the immediate reward. Several experiments have been carried out to exhibit great potentials of our method in on-site maintenance where the fault is uncertain.

This paper presents an integrated digital training twin framework for adaptive aircraft maintenance education, combining real-time competence modeling, algorithmic orchestration, and cloud–edge deployment architectures. The proposed system dynamically evaluates learner skill gaps and assigns individualized training resources through a multi-objective optimization function that balances skill alignment, Bloom’s cognitive level, fidelity tier, and time efficiency. A modular orchestration engine incorporates reinforcement learning agents for policy refinement, federated learning for privacy-preserving skill analytics, and knowledge graph-based curriculum models for dependency management. Simulation results were conducted on the Pneumatic Systems training module. The system’s validation matrix provides full-cycle traceability of instructional decisions, supporting regulatory audit-readiness and institutional reporting. The digital training twin ecosystem offers a scalable, regulation-compliant, and data-driven solution for next-generation aviation maintenance training, with demonstrated operational efficiency, instructional precision, and extensibility for future expansion.

The increasing complexity of aircraft systems and the growing global demand for certified maintenance personnel necessitate a fundamental shift in aviation training methodologies. This paper proposes a comprehensive digital-twin-based training ecosystem tailored for aviation maintenance education. The system integrates three core digital twin models: the learner digital twin, which continuously reflects individual trainee competence; the ideal competence twin, which encodes regulatory skill benchmarks; and the learning ecosystem twin, a stratified repository of instructional resources. These components are orchestrated through a real-time adaptive engine that performs multi-dimensional competence gap analysis and dynamically matches learners with appropriate training content based on gap severity, Bloom’s taxonomy level, and content fidelity. The system architecture uses a cloud–edge hybrid model to ensure scalable, secure, and latency-sensitive delivery of training assets, ranging from computer-based training modules to high-fidelity operational simulations. Simulation results confirm the system’s ability to personalize instruction, accelerate competence development, and support continuous regulatory readiness by enabling closed-loop, adaptive, and evidence-based training pathways in digitally enriched environments.

This paper presents a design method of assessment system for equipment virtual maintenance training, introduces the framework of the system, the components of each module and the scoring basis of the assessment link, and puts forward the scoring weight ratio according to the characteristics of equipment. Finally, taking the equipment as an example, the virtual assessment system is implemented.

Recently, the use of extended reality (XR) systems has been on the rise, to tackle various domains such as training, education, safety, etc. With the recent advances in augmented reality (AR), virtual reality (VR) and mixed reality (MR) technologies and ease of availability of high-end, commercially available hardware, the manufacturing industry has seen a rise in the use of advanced XR technologies to train its workforce. While several research publications exist on applications of XR in manufacturing training, a comprehensive review of recent works and applications is lacking to present a clear progress in using such advance technologies. To this end, we present a review of the current state-of-the-art of use of XR technologies in training personnel in the field of manufacturing. First, we put forth the need of XR in manufacturing. We then present several key application domains where XR is being currently applied, notably in maintenance training and in performing assembly task. We also reviewed the applications of XR in other vocational domains and how they can be leveraged in the manufacturing industry. We finally present some current barriers to XR adoption in manufacturing training and highlight the current limitations that should be considered when looking to develop and apply practical applications of XR.

The characteristics of aircraft maintenance training are that the teaching hardware resources are few, the maintenance training items are many, and the students are difficult to master. To solve this problem, we developed a simulator for aircraft maintenance training by studying the key technologies of maintenance training devices (MTD). Based on the classic control theory to design the autopilot control law, using big data from aircraft operation and scheduled maintenance to achieve high simulation MTD, to solve the difference between traditional MTD teaching and actual aircraft maintenance makes CMTD more effective and vital.

The use of brain-computer interface in neurofeedback therapy for attention deficit hyperactivity disorder (ADHD) is a relatively new approach. We conducted a randomized controlled trial (RCT) to determine whether an 8-week brain computer interface (BCI)-based attention training program improved inattentive symptoms in children with ADHD compared to a waitlist-control group, and the effects of a subsequent 12-week lower-intensity training. We randomized 172 children aged 6-12 attending an outpatient child psychiatry clinic diagnosed with inattentive or combined subtypes of ADHD and not receiving concurrent pharmacotherapy or behavioral intervention to either the intervention or waitlist-control group. Intervention involved 3 sessions of BCI-based training for 8 weeks, followed by 3 training sessions per month over the subsequent 12 weeks. The waitlist-control group received similar 20-week intervention after a wait-time of 8 weeks. The participants' mean age was 8.6 years (SD = 1.51), with 147 males (85.5%) and 25 females (14.5%). Modified intention to treat analyzes conducted on 163 participants with at least one follow-up rating showed that at 8 weeks, clinician-rated inattentive symptoms on the ADHD-Rating Scale (ADHD-RS) was reduced by 3.5 (SD 3.97) in the intervention group compared to 1.9 (SD 4.42) in the waitlist-control group (between-group difference of 1.6; 95% CI 0.3 to 2.9 p = 0.0177). At the end of the full 20-week treatment, the mean reduction (pre-post BCI) of the pooled group was 3.2 (95% CI 2.4 to 4.1). The results suggest that the BCI-based attention training program can improve ADHD symptoms after a minimum of 24 sessions and maintenance training may sustain this improvement. This intervention may be an option for treating milder cases or as an adjunctive treatment.

The maintenance of high-voltage overhead power lines involves high-risk procedures; the accidents involving live lines maintenance can be lethal. This paper presents the architecture and main features of a novel non-immersive virtual reality training system for maintenance of high-voltage overhead power lines. The general aim of this work was to provide electric utilities a suitable workforce training system to train and to certify operators working in complex and unsafe environments. The developed system has three components: the virtual warehouse, interactive 3D environments, and a learning management system. The workforce training system consists of thirty-one maintenance maneuvers, including the application of different techniques and equipment designed for various structures. Additionally, the system, using 3D animations, illustrates the safety conditions required before starting the maintenance procedures. To fit the worker’s different skill levels, the system has three operation modes: learning, practice, and evaluation, which can be accessed according to the trainee’s level of knowledge. The system is currently used to train thousands of overhead power lines operators of an electric utility in Mexico. The system has demonstrated to be a cost-effective tool for transferring skills and knowledge to new workers while reducing the time and money invested in their training.

Purpose The purpose of this study is to analyze the effect of human factor training in aircraft maintenance accident mitigation and aircraft safety in post COVID-19 aviation scenarios. The cause of aircraft accidents and details of three decades of selective aircraft maintenance accidents are analyzed to arrive to the significant aviation safety factor. The effect of COVID-19 pandemic and related technological applications to maintain high standards of safety and their applications in aircraft maintenance with respect to the view of human factors are discussed in details. Design/methodology/approach This paper details the overview of the human errors, error mitigation and need of human factor applications in aircraft maintenance industry for safe air travel. The criticality of aircraft maintenance in keeping aircraft in airworthy condition to provide safe air transportation without delay and to support airline economy is discussed in this study. Findings The cause of aircraft accidents and details of three decades of selective aircraft maintenance accidents are analyzed to arrive to the significant aviation safety factor. The effect of COVID-19 pandemic and related technological applications to maintain high standards of safety and their applications in aircraft maintenance with respect to the view of human factors are discussed in details. The route of error mitigation and need of high standard technological training with human factor knowledge, to aircraft maintenance students are analyzed in detail with the opportunity of percentages of error reduction. Originality/value This study bridges, gained knowledge for aircraft maintenance error mitigation, current accident rates and future training needs for safest air travel through high standard quality maintenance in aircraft and its systems.

The maintenance training method based on Virtual Reality (VR) and Augmented Reality (AR) technology has the characteristics of safety, no space limitation, and good reusability. Compared with the traditional training method, it can reduce the training cost, shorten the training period, and improve training effectiveness. Therefore, more and more maintenance training use VR and AR to replace training based on actual equipment to improve training effectiveness. However, in the context of multi-level tasks, there is still no clear research conclusion on how to choose training methods, maximize the advantages of each training method, and achieve higher training effectiveness. In response to this problem, this study constructed three training platforms based on VR, AR, and actual equipment, designed three maintenance tasks at different levels, and created a comparative analysis of the training effects of 60 male trainees under the three tasks and three training platforms. The results show that for single-level maintenance tasks, the training effect of the traditional group was significantly better than that of the AR group and the VR group. For multi-level maintenance tasks, the training effect of AR group was significantly better than that of the VR group. With the increasing difficulty of maintenance tasks, the training efficiency of the AR group was more than 10% higher than that of the VR group and traditional group and the AR group had less cognitive load. The conclusions of this study can provide a theoretical basis for the selection of training methods and evaluation design and help to formulate training strategies, thereby shortening the training period of professional maintenance personnel.