Surface Modification of Ammonium Polyphosphate for Enhancing Flame-Retardant Properties of Thermoplastic Polyurethane.

Currently, the development of efficient and environmentally friendly flame-retardant thermoplastic polyurethane (TPU) composite materials has caused extensive research. Ammonium polyphosphate (APP) is used as a general intumescent flame retardant to improve the flame retardancy of TPU. In this paper, we developed a functionalized APP flame retardant (APP-Cu@PDA). Adding only 5 wt% of APP-Cu@PDA into TPU can significantly improve the flame-retardant’s performance of the composite material, reflected by a high LOI value of 28% with a UL-94 test of V-0 rating. Compared with pure TPU, the peak heat release rate, total heat release, peak smoke release rate, and total smoke release were reduced by 82%, 25%, 50%, and 29%, respectively. The improvements on the flame-retardant properties of the TPU/5%APP-Cu@PDA composites were due to the following explanations: Cu2+-chelated PDA has a certain catalytic effect on the carbonization process, which can promote the formation of complete carbon layers and hinder the transfer of heat and oxygen. In addition, after adding 5% APP-Cu@PDA, the tensile strength and elongation at the break of TPU composites did not decrease significantly. In summary, we developed a new flame-retardant APP-Cu@PDA, which has better flame-retardant properties than many reported TPU composites, and its preparation process is simple and environmentally friendly. This process can be applied to the industrial production of flame retardants in the future.post-print4370 K

Investigation of magnesium hydroxide functionalized by polydopamine/transition metal ions on flame retardancy of epoxy resin.

Aiming to impart epoxy resin (EP) with flame retardancy, magnesium hydroxide (MDH) was sequentially functionalized with four transition metals and polydopamine (PDA) to prepare MDH@M-PDA (M includes Fe3+, Co2+, Cu2+, Ni2+). Compared with MDH, MDH@M-PDA presented better dispersion in EP matrix. The results illustrated that a 30 mass% of MDH@Fe-PDA imparted the EP matrix with best fire retardancy and thermal stability. Specifically, the resultant EP/MDH/MDH@Fe-PDA composites remarkably reduced flammability, which is reflected by high LOI value of 29.3% and UL-94 V-0 ratings. The peak heat release rate (PHRR) and total smoke production (TSP) were reduced by 52% and 21%, respectively. Moreover, the impact and tensile strength of EP/MDH/MDH@M-PDA composites are improved compared with EP/MDH due to the better chemical compatibility of PDA in the EP matrix. Notably, this work provided a feasible design for organo-modified MDH and enriched its practical applications of MDH as functional fillers to polymers.post-print2133 K

Ultra-High Power Density Piezoelectric Energy Harvesters

No abstract availabl

Research on the Vertical Vibration Characteristics of Hydraulic Screw Down System of Rolling Mill under Nonlinear Friction

The rolling mill with hydraulic system is widely used in the production of strip steel. For the problem of vertical vibration of the rolling mill, the effects of different equivalent damping coefficient, leakage coefficient, and proportional coefficient of the controller on the hydraulic screw down system of the rolling mill are studied, respectively. First, a vertical vibration model of a hydraulic screw down system was established, considering the nonlinear friction and parameter uncertainty of the press cylinder. Second, the correlation between different equivalent damping coefficient, internal leakage coefficient, proportional coefficient, vertical vibration was analyzed. The simulation results show that, in the closed-loop state, when Proportional-Integral-Derivative (PID) controller parameters are fixed, due to the change of the equivalent damping coefficient and internal leakage coefficient, the system will have parameter uncertainty, which may lead to the failure of the PID controller and the vertical vibration of the system. This study has theoretical and practical significance for analyzing the mechanism of vertical vibration of the rolling mill

Research on Prediction Method of Gear Pump Remaining Useful Life Based on DCAE and Bi-LSTM

As a hydraulic pump is the power source of a hydraulic system, predicting its remaining useful life (RUL) can effectively improve the operating efficiency of the hydraulic system and reduce the incidence of failure. This paper presents a scheme for predicting the RUL of a hydraulic pump (gear pump) through a combination of a deep convolutional autoencoder (DCAE) and a bidirectional long short-term memory (Bi-LSTM) network. The vibration data were characterized by the DCAE, and a health indicator (HI) was constructed and modeled to determine the degradation state of the gear pump. The DCAE is a typical symmetric neural network, which can effectively extract characteristics from the data by using the symmetry of the encoding network and decoding network. After processing the original vibration data segment, health indicators were entered as a label into the RUL prediction model based on the Bi-LSTM network, and model training was carried out to achieve the RUL prediction of the gear pump. To verify the validity of the methodology, a gear pump accelerated life experiment was carried out, and whole life cycle data were obtained for method validation. The results show that the constructed HI can effectively characterize the degenerative state of the gear pump, and the proposed RUL prediction method can effectively predict the degeneration trend of the gear pump

Remaining Useful Life Prediction of Gear Pump Based on Deep Sparse Autoencoders and Multilayer Bidirectional Long–Short–Term Memory Network

Prediction of remaining useful life is crucial for mechanical equipment operation and maintenance. It ensures safe equipment operation, reduces maintenance costs and economic losses, and promotes development. Most of the remaining useful life prediction studies focus on bearings, gearboxes, and engines; however, research on hydraulic pumps remains limited. This study focuses on gear pumps that are commonly used in the hydraulic field and develops a practical method of predicting remaining useful life. The deep sparse autoencoder is used to extract multi–dimensional features. Subsequently, the feature vectors are inputted to the support vector data description to calculate the machine degradation degree at the corresponding time and obtain the health indicator curve of the machine’s life cycle. In building the health state degradation curve, data are processed in an unsupervised manner to avoid the influence of artificial feature selection on the test. The method is validated on the public bearing and self–collected gear pump datasets. The results are better than those of the comparative algorithms: (1) commonly used time–frequency characteristics with principal component analysis and (2) deep sparse autoencoder with self–organizing mapping. Next, the multilayer bidirectional long–short–term memory network is trained as a prediction model using the gear pump health indicator curves obtained previously and applied to the test data. Finally, the proposed method is compared with two others of the same type and the evaluation indexes are calculated based on the prediction results of the three algorithms. From the evaluation indexes, the mean absolute error of the proposed method is reduced by 2.53, and the normalized mean squared error is reduced by 0.36. This result indicates that the prediction results of the method for the remaining useful life of the gear pump are closer to the actual situation

RUL Prediction of Rolling Bearings Based on a DCAE and CNN

Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the rolling bearings. The HI serves as the label of the original vibration data, and the original data with such label is input into the prediction model of the RUL based on a one-dimensional convolutional neural network (1D-CNN). The model was trained for predicting the RUL of a rolling bearing. The bearing degradation dataset was evaluated to verify the method’s effectiveness. The results demonstrate that the constructed HI can characterize the bearing degradation state effectively and that the method of predicting the RUL can accurately predict the bearing degradation trend

Study on a Fault Identification Method of the Hydraulic Pump Based on a Combination of Voiceprint Characteristics and Extreme Learning Machine

Aiming at addressing the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an axial piston pump fault diagnosis method that is based on the combination of Mel-frequency cepstrum coefficients (MFCC) and the extreme learning machine (ELM) is proposed. Firstly, a sound sensor is used to realize contactless sound signal acquisition of the axial piston pump. The wavelet packet default threshold denoises the original acquired sound signals. Afterwards, windowing and framing are added to the de-noised sound signals. The MFCC voiceprint characteristics of the processed sound signals are extracted. The voiceprint characteristics are divided into a training sample set and test sample set. ELM models with different numbers of neurons in the hidden layers are established for training and testing. The relationship between the number of neurons in the hidden layer and the recognition accuracy rate is obtained. The ELM model with the optimal number of hidden layer neurons is established and trained with the training sample set. The trained ELM model is applied to the test sample set for fault diagnosis. The fault diagnosis results are obtained. The fault diagnosis results of the ELM model are compared with those of the back propagation (BP) neural network and the support vector machine. The results show that the fault diagnosis method that is proposed in this paper has a higher recognition accuracy rate, shorter training and diagnosis times, and better application prospect