The large petrochemical pelletizer gearbox is a critical component in synthetic chemicals, accurate forecasting of its time series from online oil monitoring is of utmost importance for safe operation. However, existing time series forecasting methods face challenges in handling oil monitoring scenarios characterized by disturbances, multimodality, and large time spans. To address these issues, we propose an intelligent forecasting method named multiscale inverted transform network (MITN). First, the multimodal online oil monitoring time series collected from moisture sensors, viscosity sensors, abrasive image sensors, and metal particle sensors are utilized to perform correlation analysis and identify the key nonlinear variables. In addition, the multiscale module is further employed to obtain comprehensive redundant characteristics. Second, the inverted perspective is designed for modeling from the time dimension and the variable dimension. Third, the multiple multiattention mechanism module is utilized with the feedforward network to learn the semantic meaning of variable time series and produce accurate forecasting results. Finally, layer normalization is used to improve the training stability and convergence to eliminate the distribution difference between variables. We used online oil monitoring time series from a petrochemical pelletizer gearbox from July 2018 to May 2023 for validation. The results show that MITN not only can obtain smaller forecasting errors than the existing time series forecasting networks, such as long short-term memory, gated recurrent unit, temporal convolutional network, and transform, but also can effectively generalize to the unknown variables. The proposed MITN pioneers ideal multivariate time series forecasting for complex online oil monitoring, with the potential to enhance the operational safety of large petrochemical plants.

The existing fault diagnosis of harmonic drives is difficult to deploy vibration sensors, and the diagnosis accuracy is insufficient only using current signal. Therefore, we propose an intelligent fault diagnosis method using a multimodal collaborative meta network (MCMN) with severely missing modality. First, multimodal data of the harmonic drive were collected to analyze. Second, a feature reconstruction network is used to achieve a unified model to handle missing modalities in testing. Besides, the uncertainty assessment is used as a feature regularization network to overcome data bias. Third, the inference of the meta-learning is used to obtain the generated weights, and carried out the joint optimization. Finally, MCMN is optimized by using the approximate lower bound of Monte Carlo. Experimental results show that the accuracy of MCMN is 93.75% in the case of full-modalities training data and the severely missing modalities testing data, which is better than the existing method.

Soft robot has demonstrated promise in unstructured and dynamic environments due to unique advantages, such as safe interaction, adaptiveness, easy to actuate, and easy fabrication. However, the highly dissipative nature of elastic materials results in small stiffness of soft robot which limits certain functions, such as force transmission, position accuracy, and load capability. In this paper, we present a novel articulated soft robot with variable stiffness. The robot is constructed by rigid joints and compliant bistable structures in series. Each joint can be independently locked through triggering the bistable structure to touch the mechanical constrain. Thus, the bending stiffness of the joint can be magnified which increases the stiffness of the articulated soft robot. Through this construction method, even driven by only one servomotor, the robot demonstrates variable workspace and stiffness which have the potential of dexterous manipulation and maintaining shape under tip load.

How muscles are used is a key to understanding the internal driving of fish swimming. However, the underlying mechanisms of some features of the muscle activation patterns and their differential appearance in different species are still obscure. In this study, we explain the muscle activation patterns by using 3D computational fluid dynamics models coupled to the motion of fish with prescribed deformation and examining the torque and power required along the fish body with two primary swimming modes. We find that the torque required by the hydrodynamic forces and body inertia exhibits a wave pattern that travels faster than the curvature wave in both anguilliform and carangiform swimmers, which can explain the traveling wave speeds of the muscle activations. Notably, intermittent negative power (i.e., power delivered by the fluid to the body) on the posterior part, along with a timely transfer of torque and energy by tendons, explains the decrease in the duration of muscle activation towards the tail. The torque contribution from the body elasticity further clarifies the wave speed increase or the reverse of the wave direction of the muscle activation on the posterior part of a carangiform swimmer. For anguilliform swimmers, the absence of the aforementioned changes in the muscle activation on the posterior part is consistent with our torque prediction and the absence of long tendons from experimental observations. These results provide novel insights into the functions of muscles and tendons as an integral part of the internal driving system, especially from an energy perspective, and they highlight the differences in the internal driving systems between the two primary swimming modes.

In this paper, a concentric wire-driven mechanism (CWM) is presented. The CWM comprises of two nested wire-driven mechanisms (WDM). Each WDM contains a flexible backbone and a set of wires. The backbone bending is controlled by pulling the wires. The stiffness of the outer WDM is controllable and dominants that of the overlapped section. Therefore, in the overlapped section the inner WDM conforms to the shape of the outer WDM and the tip of the outer WDM serves as the base of the distal separate section of the inner WDM. Compared with conventional flexible mechanisms, i.e., tendon/wire/cable-driven mechanism and concentric tube mechanism, the CWM has a much wider workspace. This advantage is demonstrated by simulations based on kinematic modeling. A prototype is developed and the preliminary experimental results validate the design concept.

In this paper, a novel constrained tendon-driven serpentine manipulator (CTSM) suited for minimally invasive surgery is presented. It comprises of a flexible backbone, a set of controlling tendons and a constraint. In the CTSM not only the curvature of the bending section can be controlled but also the length. Specifically, the curvature is controlled by the tendons, and the length is controlled by a constraint tube, which is translational and is concentric with the flexible backbone. The kinematic model of the CTSM is developed based on the piecewise constant curvature assumption. Analysis shows that by introducing the translational constraint both the workspace and dexterity of the manipulator are improved. The stiffer the constraint the larger the workspace expansion and the smaller the dexterity enhancement. A prototype is developed and the experimental results validate the design idea and analysis.

This paper presents a novel double-hull boat with two wire-driven flapping propulsors. The boat comprises two identical hulls and a control center. Each hull is propelled by a biomimetic wire-driven flapping propulsor, which has seven joints, and is driven by one servo motor. Propulsion model of the boat is developed by integrating the reaction force during the flapping cycle. Motion of the boat is studied, including cruising and turning around. Two cruising modes and three turning modes are presented. Results show that the boat cruises forward steadily. In the tests, the maximum speed of the boat is 0.323 BL/s; the minimum turning radius of the boat is 0.517 BL; the maximum turning speed is 13.9°/s.

This paper presents the idea of employing constraints to expand the workspace of Underactuated Flexible Manipulators (UFM). The constraints are divided into two types: bilateral constraint (BC) and unilateral constraint (UC). Under the constraints the UFM is segmented into two sections: the anterior constrained section and the distal free section; moreover, the motion of the UFM is altered, i.e., the end effector of the UFM can get access to positions where unreachable previously, or, the workspace is expanded. In the paper, constrained kinematics model is developed at first. The workspace is derived thereafter. To validate the idea, an underactuated wire-driven serpentine manipulator is built. In the experiments, the two types of constraints are applied. Experiment results agree well with the simulation results.

This paper presents a novel robot fish with vector propulsion. It can swim like a shark and/or a dolphin. The propulsor (tail) of the robot has an underactuated serpentine backbone and the actuation is done by two sets of orthogonally distributed wires. The backbone is composed of seven vertebras and an elastic rod. The vertebras are articulated by the rod and spherical joints. The horizontal flapping and vertical flapping are independently actuated by two motors. This enables the propulsor providing thrust in all directions. Propulsion model of the propulsor is developed by integrating the kinematic model and Lighthill's elongated body theory. A prototype is built. Tests show that the robot fish could flap its tail like the shark or the dolphin effectively. In the swimming tests, the maximum swimming speed of the robot is 0.35 BL/s.

Condition monitoring and fault diagnosis in modern manufacturing automation is of great practical significance. It improves quality and productivity, and prevents damage to machinery. In general, this practice consists of two parts: 1)extracting appropriate features from sensor signals and 2)recognizing possible faulty patterns from the features. Through introducing the concept of marginal energy in signal processing, a new feature representation is developed in this paper. In order to cope with the complex manufacturing operations, three approaches are proposed to develop a feasible system for online applications. This paper develops intelligent learning algorithms using hidden Markov models and the newly developed support vector techniques to model manufacturing operations. The algorithms have been coded in modular architecture and hierarchical architecture for the recognition of multiple faulty conditions. We define a novel similarity measure criterion for the comparison of signal patterns which will be incorporated into a novel condition monitoring system. The sensor-based intelligent system has been implemented in stamping operations as an example. We demonstrate that the proposed method is substantially more effective than the previous approaches. Its unique features benefit various real-world manufacturing automation engineering, and it has great potential for shop floor applications.

To achieve the desired quality in plastic injection molding, advanced monitoring techniques are often recommended in the workshop. Unfortunately, the signal in plastic injection modeling process such as nozzle pressure that is relevant to part quality is not easy to obtain because of the cost of sensors. The sensor-based modeling idea is therefore adopted. In this paper, a new method for predicting the parts weight in plastic injection molding using least squares support vector regression (LS-SVR) is proposed, which is composed of two steps. The first step is to estimate the nozzle pressure with the hydraulic system pressure using an LS-SVR model. The second step is to predict product weight using the estimated nozzle pressure, which is done using another LS-SVR model. The experimental results show that the new method is very effective.

In this paper, a new approach is proposed using the 3-D thermal energy distribution of the workpiece. The new approach is based on the fact that during the stamping process, the workpiece absorbs energy to deform. This mechanical energy is converted into thermal energy. Therefore, it is possible to diagnose the stamping by analyzing the 3-D thermal energy distribution. In practice, the thermal energy distribution can be acquired using an infrared camera. However, it is often necessary to reconstruct the 3-D thermal energy distribution, which can be accomplished by using the Octree algorithm. On the other hand, one can compute the thermal energy distribution through finite-element modeling (FEM) as the baseline for diagnosis. Then, by comparison, the diagnosis can be carried out. A couple of examples are presented in detail together with the experiment validation. Note to Practitioners-These days computer-aided design (CAD) and finite-element modeling (FEM) are widely used for sheet metal stamping, especially in the automotive and aerospace industries. However, fault diagnosis and die tryout still rely heavily on the experiences of the engineers/machinists on the shop floor. An existing diagnosis method compares the strain distribution calculated using FEM and the experimental strain distribution, which is calculated from the measured deformation of the initial grid pattern. However, this method is time consuming as it requires the initial grid pattern preparation by either chemical etching or laser marking. This paper presents a new method for diagnosing sheet metal stamping processes. The new method compares the thermal distribution calculated using FEM and the experimental thermal distribution result, which can be acquired using an infrared (IR) camera. Therefore, it is more convenient to use it on the shop floor. The thermal FEM can be found based on strain FEM. However, the infrared image is 2-D and, hence, 3-D thermal distribution reconstruction is often necessary. This paper includes several practical examples. Some practical concerns, such as the conduction heat lost to the environment and the infrared camera viewing angles, are also discussed

Condition monitoring and fault diagnosis in modern engineering practices is of great practical significance for improving the quality and productivity, preventing the machinery from damages. In general, this practice consists of two parts: extracting appropriate features from sensor signals and recognizing possible faulty patterns from the features. In order to cope with the complex manufacturing operations and develop a feasible system for real-time application, we proposed three approaches. By defining the marginal energy, a new feature representation emerged, while by real-time learning algorithms with support vector techniques and hidden Markov model representations, a modular software architecture and a new similarity measure were developed for comparison, monitoring, and diagnosis. A novel intelligent computer-based system has been developed and evaluated in over 30 factories and numerous metal stamping processes as an example of manufacturing operations. The real-time operation of this system demonstrated that the proposed system is able to detect abnormal conditions efficiently and effectively resulting in a low-cost, effective approach to real-time monitoring in manufacturing. The related technologies have been transferred to industry, presenting a tremendous impact in current automation practice in Asia and the world.

It is very important to use a reliable and inexpensive sensor to obtain useful information about manufacturing processing, such as cutting force for monitoring automated machining. In this paper, the feed-cutting force is estimated using inexpensive current sensors installed on the ac servomotor of a computerized numerical control (CNC) turning center, with the results applied to the intelligent tool wear monitoring system. The mathematical model is used to disclose the implicit dependency of feed-cutting force on feed-motor current and feed speed. Afterwards, a neuro-fuzzy network is used to identify the cutting force with current measurement only. This hybrid math-fuzzy approach will reduce the modeling uncertainty and measurement cost. Finally, the estimated cutting force is applied in the tool-wear monitoring process. Successful experiments demonstrate robustness and effectiveness of the suggested method in the wide range of tool-wear monitoring applications.