A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method (E-CICA) is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform(DWT) method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio(ER) of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals.
Additive manufacturing technology can make products of arbitrary shapes, greatly expanding the design space of lattice structures. Compared with traditional solid lattice structures, the lattice structures with hollow struts have higher flexural strength, which arouses interests of designers recently. However, owing to the more complex shapes of structures, the model generation and design are facing more challenges. In this article, a novel generative design method for the creation of lattice structures with hollow struts is proposed. This method consists of three stages: initialization, analysis, and optimization. First of all, a ground structure is generated automatically based on initial conditions. Then, the finite element analysis is used to get the stress and coordinate information of finite element nodes as well as deformation information of the ground structure. At last, a rapid optimization method is presented based on the idea of mapping the strut equivalent stress to the strut wall thickness to optimize materials distribution. The proposed method is validated through a case study, demonstrating that this method can enhance performance of products while reducing the complexity of the optimization problem.
Purpose Orientation determination is an essential planning task in additive manufacturing (AM) because it directly affects the part quality, build time, geometric tolerance, fabrication cost, etc. This paper aims to propose a negative feedback decision-making (NFDM) model to realize the personalized design of part orientation in AM process. Design/methodology/approach NFDM model is constructed by integrating two sub-models: proportional–integral–derivative (PID) negative feedback control model and technique for order preference by similarity to an ideal solution (TOPSIS) decision-making model. With NFDM model, a desired target is first specified by the user. Then, the TOPSIS decision model calculates the “score” for the current part orientation. TOPSIS decision model is modified for ease of control. Finally, the PID controller automatically rotates the part based on the error between the user-specified target and the calculated “score”. Part orientation adjustment is completed when the error is eliminated. Five factors are considered in NFDM model, namely, surface roughness, support structure volume, geometric tolerance, build time and fabrication cost. Findings The case studies of turbine fan and dragon head indicate that the TOPSIS model can be perfectly integrated with the PID controller. This work extends the proposed model to different AM processes and investigates the feasibility of combining different decision-making models with PID controller and the effects of including various evaluation criteria in the integrated model. Originality/value The proposed model innovatively takes the TOPSIS decision-making model and the PID control model as a whole. In this way, the uncontrollable TOPSIS model becomes controllable, so the proposed model can control the TOPSIS model to achieve the user-specified targets.
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