Critical Machine Research Articles

Learning Generative Socially Aware Models of Pedestrian Motion

Among the biggest challenges faced when integrating robots and intelligent vehicles in crowded environments is the difficulty encountered by artificial agents in anticipating people's moves, which is indeed a critical machine reasoning task in order to enable safe and comfortable interaction with humans. In general terms, this is a multi-agent prediction problem, whose solution, from a Bayesian perspective, involves on one hand inferring the joint latent dynamical state of all agents conditioned on observed data and on the other hand learning the corresponding system dynamics. Among the advantages of such a probabilistic approach is the fact that it naturally allows to account for the intrinsic stochasticity of both human behavior and sensor data in a principled manner. In this letter, we propose a novel Bayesian generative model, which generalizes a popular class of deterministic multiagent motion models and is capable of encoding traffic agents' interactions in a latent probabilistic space. We demonstrate, using real pedestrian data-sets, how it can be exploited for inference, prediction, and trained for learning of agent interaction dynamics. Our experimental evaluations indicate that the proposed approach outperforms both probabilistic latent variable models that neglect multi-agent interactions as well as deterministic physics-inspired models of human motion in crowds.

An Accurate and Fast Start-Up Scheme for Power System Real-Time Emergency Control

With the development of PMUs in power systems, the response-based real-time emergency control becomes a promising way to prevent power outages when power systems are subjected to large disturbances. The first step in the emergency control is to start up accurately and fast when needed. To this end, this paper proposes a well-qualified start-up scheme for the power system real-time emergency control. Three key technologies are proposed to ensure the effectiveness of the scheme. They are, an instability index, a Critical Machines (CMs) identification algorithm, and a two-layer single machine infinite bus (SMIB) equivalence framework. The concave–convex-area-based instability index shows good accuracy and high reliability, which is used to identify the transient instability of the system. The CMs identification algorithm can track the changes of CMs and form the proper SMIB system at each moment. The new two-layer SMIB equivalence framework, compared with conventional ones, can significantly reduce the communication burden and improve the computation efficiency. The simulations in two test power systems show that the scheme can identify the transient instability accurately and fast to restore the system to stability after the emergency control. Besides, the proposed method is robust to measurement errors, which enhances its practicality.

Estudio de la producción en un aserradero de corte cualitativo a través del uso de simulación de eventos discretos y diseño de experimentos

Introduction: When sawing logs without diameter sorting, the cutting program generates decisions and routes depending on the log’s appearance and machine load. Objective: To optimize the production flow of a custom-cut Pinus radiata D. Don sawmill, through detection and analysis of critical machines, using discrete event simulation and experimental design. Materials and methods: Two simulation models were developed for sawing logs with lengths of 2.5 and 5.0 m, and diameters between 34 and 44 cm to produce sawnwood with a thickness of 5/4 with variable width. The number of logs processed per shift was the response variable of the model. Heavily-used machines and transports with long queues were candidates for critical machines. The impact on production was determined by means of experimental design, where the factors assessed were: (A) delivery from bandsaw carriage 1 to band saw 3, (B) increased debarker capacity, (C) failures eliminated from bandsaw 1 and 2, and (D) direct delivery from bandsaw 2 to edger 2. Results and discussion: Modifications to the production flow were proposed because factors B, C and D significantly increased (P = 0.1) the production level (logs per shift); the increases over the sawmill’s initial condition were 13 and 18 % for lengths of 2.5 and 5.0 m, respectively. Conclusion: Simulation and experimental design can be applied in small and medium-sized sawmills to improve production when processing logs without diameter sorting.

Elucidation of ball bearing performance utilizing product functions of vibration signals and locality sensitive discriminant analysis

Dimensionality is a decent term utilized for the dimension-related issues of feature vectors for the condition assessment of bearings. It has been a challenging task while dealing with the characteristic information conservation of the sampled data. Feature vectors with higher dimensions provide an accurate description of the condition, while lower dimension vectors are easy to be classified. These conditions make the dimensionality of feature space a big challenge in the performance evaluation of critical machine parts like bearings. This paper presents a judicious and wise application of a linear approach, locality sensitive discriminant analysis along with the rational use of local mean decomposition in performance degradation assessment. The combination effectively solves the problems of insufficient training samples and large dimensionality of fault features, which imparts excessive noise and causes loss of competent hidden characteristics. In such cases, local structures of feature space are more crucial than the global one. These obstacles can be answered by imparting locality sensitive discriminant analysis as an initial operation. Locality sensitive discriminant analysis is a linear dimensionality reduction tool, which explores the precise projections that amplify the margin between data points and prepare a conservation aid to preserve faulty information of bearings. The steps are followed to achieve the same: decomposition of vibration signal into product functions; calculation of fault features; third, higher dimensionality of the features is reduced with the implementation of locality sensitive discriminant analysis and some prime features are selected with the proposed criterion; the reduced features are further clustered and a trained model of assessment is prepared. Finally, health indicator is calculated from the trained model and test features. The proposed technique is verified on two bearing datasets. The superiority of the technique has been envisaged by comparing the method with different similar assessment methods i.e. time domain features, linear discriminant analysis, and principal component analysis.

Evolutionary algorithms for multi-objective dual-resource constrained flexible job-shop scheduling problem

This paper presents a multi-objective dual-resource constrained flexible job-shop scheduling problem (MODRCFJSP) with the objectives of minimizing the makespan, critical machine workload and total workload of machines simultaneously. Two types of multi-objective evolutionary algorithms including fast elitist non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are proposed for solving MODRCFJSP. Some efficient mutation and crossover operators are adapted to the special chromosome structure of the problem for producing new solutions in the algorithm’s generations. Besides, we provide controlled elitism based version of NSGA-II and NRGA, namely controlled elitist NSGA-II (CENSGA-II) and controlled elitist NRGA (CENRGA), to optimize MODRCFJSP. To show the performance of the four proposed algorithms, numerical experiments with randomly generated test problems are used. Moreover, different convergence and diversity performance metrics are employed to illustrate the relative performance of the presented algorithms.

Three-Dimensional Thermo-Elasto-Hydrodynamic Computational Fluid Dynamics Model of a Tilting Pad Journal Bearing—Part II: Dynamic Response

Abstract Part II presents a novel approach for predicting dynamic coefficients for a tilting pad journal bearing (TPJB) using computational fluid dynamics (CFD) and finite element method (FEM), including fully coupled elastic deflection, heat transfer, and fluid dynamics. Part I presented a similarly novel, high fidelity approach for TPJB static response prediction which is a prerequisite for the dynamic characteristic determination. The static response establishes the equilibrium operating point values for eccentricity, attitude angle, deflections, temperatures, pressures, etc. The stiffness and damping coefficients are obtained by perturbing the pad and journal motions about this operating point to determine changes in forces and moments. The stiffness and damping coefficients are presented in “synchronously reduced form” as required by American Petroleum Institute (API) vibration standards. Similar to Part I, an advanced three-dimensional thermal—Reynolds equation code validates the CFD code for the special case when flow Between Pad (BP) regions is ignored, and the CFD and Reynolds pad boundary conditions are made identical. The results show excellent agreement for this validation case. Similar to the static response case, the dynamic characteristics from the Reynolds model show large discrepancies compared with the CFD results, depending on the Reynolds mixing coefficient (MC). The discrepancies are a concern given the key role that stiffness and damping coefficients serve instability and response predictions in rotordynamics software. The uncertainty of the MC and its significant influence on static and dynamic response predictions emphasizes a need to utilize the CFD approach for TPJB simulation in critical machines.

Degradation state mining and identification for railway point machines

Critical point machine failure in railway-signal systems can lead to fatal accidents. Hence, early identification of anomalies is vital in guaranteeing reliable and safe transportation. However, most of the existing early fault diagnosis methods can only estimate the degradation trend under a specific fault mode. How to analyze the diversified degradation conditions under multiple fault modes is still a key problem. Considering the diversity of fault modes, this study proposes an early fault diagnosis method based on self-organizing feature map network and support vector machine, focusing on the use of non-fault data to simultaneously mine and accurately identify degradation states under different fault modes, to provide guidance for proactive machine maintenance. The experimental results obtained via application of this scheme to field data for railway point machines demonstrate that the proposed methodology can effectively mine and accurately identify degradation states with different machine characteristics.

On Proactive, Transparent, and Verifiable Ethical Reasoning for Robots

Previous work on ethical machine reasoning has largely been theoretical, and where such systems have been implemented, it has, in general, been only initial proofs of principle. Here, we address the question of desirable attributes for such systems to improve their real world utility, and how controllers with these attributes might be implemented. We propose that ethically critical machine reasoning should be proactive, transparent, and verifiable. We describe an architecture where the ethical reasoning is handled by a separate layer, augmenting a typical layered control architecture, ethically moderating the robot actions. It makes use of a simulation-based internal model and supports proactive, transparent, and verifiable ethical reasoning. To do so, the reasoning component of the ethical layer uses our Python-based belief–desire–intention (BDI) implementation. The declarative logic structure of BDI facilitates both transparency, through logging of the reasoning cycle, and formal verification methods. To prove the principles of our approach, we use a case study implementation to experimentally demonstrate its operation. Importantly, it is the first such robot controller where the ethical machine reasoning has been formally verified.

FOC Transformation for Single Open-Phase Faults in the Five-Phase Open-End Winding Topology

Many critical five-phase permanent-magnet synchronous machine drive systems require operation after a single open-phase fault. The single dc supply open-end winding inverter topology allows for the injection of four unbalanced phase currents to better optimize the postfault machine torque after this fault. However, all previously published field-oriented control schemes send varying current references to the proportional–integral (PI) current controllers when controlling four unbalanced phase currents. This makes the design of the PI controllers considerably difficult as each machine operating point will require a different gain to accurately track the varying reference with minimum ripple. Consequently, this paper proposes a new postfault transformation matrix that obtains constant current references for any given set of four unbalanced sinusoidal phase currents. This facilitates easy design of the PI current controllers as the steady-state error can be reduced to zero in a user-defined response time for all operating points. This current control technique is the first to be designed specifically for the case of an open-phase fault to a five-phase open-end winding system.

Analysis of critical machine reliability in manufacturing cells

Purpose: In an increasingly competitive business environment, machine reliability problem merits special attention in operations of manufacturing cells. This is mainly due to flow line nature of the cellular layout, interdependency of downstream and upstream of machines related to each other. This study investigates the effect of critical machine reliability improvement on production capacity and throughput time in manufacturing cells. Design/methodology/approach: A discrete-event simulation model was developed to investigate the effectiveness of a reliability plan focusing on the most critical production machines in improving the performance level as an alternative to increasing the reliability of all machines. Four machine criticality policies are examined in the simulation experiments.Findings: The results of this experimental study indicated that an improvement of reliability of a limited number of machines leads to an increase in overall production capacity and speed in cellular manufacturing operations. A reliability plan, that focuses on a set of critical machines, potentially offers a more economical alternative to increasing the reliability of all machines in such facility.Research limitations/implications: The results demonstrate that to achieve higher production capacity and shorter throughput times, managers should consider directing more resources to increase the reliability of critical machines, particularly, those with shorter mean time to failure and higher utilization.Originality/value: The designed simulation model is unique in representing the dynamics of a real world manufacturing cell environment by encoding operational functions such as machine failure, maintenance resource allocation, material flow, job sequencing and scheduling. A new machine availability metric is defined as well.

Transient Stability Assessment Using Individual Machine Equal Area Criterion PART III: Reference Machine

The analysis of the transient stability of a multimachine system via the use of a few machines is a promising technique for transient security assessment. This paper proposes a genuine nonglobal direct-time-domain method that is developed from the previous COI-reference-based IMEAC method. In this paper, the reference machine (RM) is chosen as a real machine that does not separate from the system after fault clearing. The key concept of this paper is to select the reference machine (RM) first and then replace the COI with the RM during the post-fault period in the transient security assessment. In this manner, the motions of all machines in the system are redefined in RM reference. Furthermore, it is found that the Kimbark curve of the RM-reference-based critical machine has a distinctive quasi-sinusoidal characteristic, and thus the curve is predictable after fault clearing. Following the individual-machine thinking, the stability analysis of the multimachine system is transformed into monitoring the stability state of a few RM-reference-based critical machines, which completely avoids the usage of COI. The simulation results show the effectiveness of the proposed method in the transient stability analysis of multimachine systems.

Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data

Often, manufacturing equipment is utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost. The emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In this paper, the AI-based algorithms for predictive maintenance are presented, and are applied to monitor two critical machine tool system elements: the cutting tool and the spindle motor. A data-driven modeling approach will be described, and it will be utilized to investigate the tool wear and the bearing failures.

5G-Based Smart Healthcare Network: Architecture, Taxonomy, Challenges and Future Research Directions

Healthcare is undergoing a rapid transformation from traditional hospital and specialist focused approach to a distributed patient-centric approach. Advances in several technologies fuel this rapid transformation of healthcare vertical. Among various technologies, communication technologies have enabled to deliver personalized and remote healthcare services. At present, healthcare widely uses the existing 4G network and other communication technologies for smart healthcare applications and are continually evolving to accommodate the needs of future intelligent healthcare applications. As the smart healthcare market expands the number of applications connecting to the network will generate data that will vary in size and formats. This will place complex demands on the network in terms of bandwidth, data rate, and latency, among other factors. As this smart healthcare market matures, the connectivity needs for a large number of devices and machines with sensor-based applications in hospitals will necessitate the need to implement Massive-Machine Type Communication. Further use cases such as remote surgeries and Tactile Internet will spur the need for Ultra Reliability and Low Latency Communications or Critical Machine Type Communication. The existing communication technologies are unable to fulfill the complex and dynamic need that is put on the communication networks by the diverse smart healthcare applications. Therefore, the emerging 5G network is expected to support smart healthcare applications, which can fulfill most of the requirements such as ultra-low latency, high bandwidth, ultra-high reliability, high density, and high energy efficiency. The future smart healthcare networks are expected to be a combination of the 5G and IoT devices which are expected to increase cellular coverage, network performance and address security-related concerns. This paper provides a state-of-the-art review of the 5G and IoT enabled smart healthcare, Taxonomy, research trends, challenges, and future research directions.

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

With the rapid advancement and widespread applications of information technology in the manufacturing shop floor, a huge amount of real-time data is generated, providing a good opportunity to effectively respond to unpredictable exceptions so that the productivity can be improved. Thus, how to schedule the manufacturing shop floor for achieving such a goal is very challenging. This paper addresses this issue and a new multiagent-based real-time scheduling architecture is proposed for an Internet of Things-enabled flexible job shop. Differing from traditional dynamic scheduling strategies, the proposed strategy optimally assigns tasks to machines according to their real-time status. A bargaining-game-based negotiation mechanism is developed to coordinate the agents so that the problem can be efficiently solved. To demonstrate the feasibility and effectiveness of the proposed architecture and scheduling method, a proof-of-concept prototype system is implemented with Java agent development framework platform. A case study is used to test the performance and effectiveness of the proposed method. Through simulation and comparison, it is shown that the proposed method outperforms the traditional dynamic scheduling strategies in terms of makespan, critical machine workload, and total energy consumption.

Hybrid heuristic algorithm for multi-objective scheduling problem

This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search (HS) algorithm, namely the oppositional global-based HS (OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems (MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning (OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory (HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution (TOPSIS), are implemented for solving the MOFJSP. Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies. Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.

PERENCANAAN PEMELIHARAAN MESIN PRODUKSI DENGAN MENGGUNAKAN METODE RELIABILITY CENTERED MAINTENANCE (RCM) (Studi Kasus: PT. S)

PT. S is a company that produces steel pipe. As a manufacturing company, the company relies heavily on its production machines, one of the main engines in this company is mill 303. However, the high frequency of failure and downtime caused the production decreased. This research use reliability centered maintenance (RCM) method. This method is used to determine the machine maintenance schedule as well and the interval optimal time. The stages of this research begin with determining the critical machine then make the system description followed by FMEA analysis, ABC analysis to determine critical component, FTA analysis, analysis of time distribution up to interval optimum maintenance. The result of this research is known 11 critical component with highest RPN value. 11 components that have been identified then analyzed the distribution of the time to failure and distribution of the time to repair and get the value of MTTR and MTTF. Bearing drive roll is the component with the highest RPN value of 512, the MTTR value of 3.56259, the MTTF value of 730,537 and the optimal maintenance interval of 504.64 hours.

Availability analysis of distillery plant using petri nets

PurposeThe purpose of this paper is to prepare a methodology to evaluate availability analysis of distillery plant using Petri nets (PN). The effect of various failures, repair rate and availability of repair facilities on the system has been studied.Design/methodology/approachThe PN model of a system is developed to study the dynamic behavior of the system under various working conditions. Availability analysis of the plant is presented taking into consideration of failure and repair rates of their subsystems and has been carried out using Monte Carlo simulation.FindingsThe bottle washing machine is the most critical machine from a maintenance point of view which has more compact on the system performance as compared to other machines. The availability analysis of distillery plant helps the management to adopt a suitable maintenance policy to improve upon plant production and plant availability.Originality/valueThe application of this proposed PN-based approach is very useful in finding the most critical subsystem and its effect on the performance of the system in terms of availability and plant production. Further, the advantage of the PN approach is that dynamic behavior of the system under consideration is modeled with graphic representation of the system and given distribution can be associated with the subsystems for analysis. The superiority of this approach over others, such as network, fault tree and Markov analysis, is outlined in the paper.

A Differential Optical Sensor for Non-Invasive Real-Time Monitoring of Ultrafiltration Rate in Hemofiltration Therapies

Hemofiltration (HF) is a group of blood purification therapies used to treat patients with kidney injury. HF works using a process called ultrafiltration (UF) that removes excess liquid accumulated in the patient’s body caused by lack of excretion. UF progress is monitored by the HF machine, but the state-of-the-art method is cumbersome and could be more accurate. In this paper, a system composed by two optical sensors is proposed for real-time non-invasive estimation of ultrafiltration rate. This new system is simple, rugged, and low-cost and operates on sound theoretical foundations. The sensor system has been tested with two different experimental protocols and showed good correlation between its output and the reference value of the ultrafiltration rate (R2 = 0.97), as well as improved accuracy compared with the available commercial machine ( $\simeq 12$ ml/h). This system also has the potential to simplify the architecture required by critical care blood purification machines to perform UF control.

Plant Layout Optimization in Steel Forging Industry by CORELAP Algorithm

Objectives: The productivity problem encountered by a steel forging industry owing to its capacity enhancement has been bated for this research work. The study focused to identify a solution by optimizing the material flow in between the critical machines through the radical redesign of the plant layout. Methods: The plant layout determines the way in which the materials flow through the operation. Analysis of the layout data has done with relationship chart and closeness rating. The equipment for manufacturing was counted in terms of the direction of material flow and the time taken for the production process. The operation process chart, material flow pattern and activity relationship chart have been used in layout analysis through Computerized Relationship Layout Planning (CORELAP) method. It optimizes the material movement between the equipment and identifies the relative position of the various machineries in the processing area. A new model has been developed by CORELAP and simulation has done using the software Arena. Findings: The performance loss in the material flow pattern has been identified in the forging section of the plant. On the analysis with relationship chart and closeness rating, it is identified and this is happened with the extra time taken for the material flow by the imprecise positions of machines. The work flow pattern in the forging section is quite longer and unnecessary material transportation occurred there. Fifteen machines were relocated and optimized the production flow with CORELAP algorithm. The simulation study has conducted using Arena and the value-added time taken to produce each product in dissimilar machines in the modified layout is identified. Novelty/Improvement: The modified layout delivered a value addition in material flow pattern and shows an estimated improvement in productivity value of 7.98 percentages.

Development of a Preventive Maintenance Strategy for an Automatic Production Line Based on Group Maintenance Method

The high maintenance costs and low reliability of automatic production line are attributed to the complexity of maintenance management. In the present study, a preventive maintenance strategy for the automatic production line was developed based on the group maintenance method. The criticality of machines in the production line was evaluated, and then the machines were classified into three groups: the most critical machines, the secondary critical machines and the general machines. The general machines were performed on the breakdown maintenance. The preventive maintenance model of the most critical machines was established with the shortest shutdown time as decision objective on basis of the Delay-time theory. The maintenance model of the secondary critical machine was established based on the considering of reliability-maintenance cost. A case study on an automotive part automatic production line was carried out to verify the proposed preventive maintenance strategy based on the production line data, and the maintenance periods of the most and secondary critical machines were gained; meanwhile, the machines all satisfied the reliability requirements during the maintenance periods.