Multiple Fault Diagnosis Method in Multi-Station Assembly Processes Using State Space Model and Orthogonal Diagonalization Analysis

Fault diagnosis for underdetermined multistage assembly processes via an enhanced Bayesian hierarchical model

This paper presents a novel approach for optimal key characteristics-based sensor distribution in a multi-station assembly process, for the purpose of diagnosing variation sources responsible for product quality defects in a timely manner. Current approaches for sensor distribution are based on the assumption that measurement points can be allocated at arbitrary locations on the part or subassembly. This not only presents challenges in the implementation of these approaches but additionally does not allow required product assurance and quality control standards to be integrated with them, due to lack of explicit relations between measured features and geometric dimensioning and tolerancing (GD&T). Furthermore, it causes difficulty in calibration of measurement system and increases the likelihood of measurement error due to the introduction of measurement points not defined in GD&T. In the proposed approach, we develop methodology for optimal sensor allocation for 6-sigma root cause analysis that maximizes the number of measurement points placed at critical design features called Key Characteristics (KCs) which are classified into: Key Product Characteristics and Key Control Characteristics and represent critical product and process design features, respectively. In particular, KCs have defined dimensional and geometric tolerances which provides necessary design reference model for process control and diagnosis of product 6-sigma variation faults. The proposed approach allows obtaining minimum required production system 6-sigma diagnosability. A feature-based procedure is proposed which includes Genetic Algorithm-based approach (allowing pre-defined KCs as the measurement points) and state-of-the-art approaches (unrestricted location of measurement points) to iteratively include arbitrary measurement points together with KCs in the final sensor layout. A case study of automotive assembly processes is used to illustrate the proposed feature-based approach.

Linearization is commonly employed in modeling of multi-station manufacturing processes for quality improvement purposes. However, an analysis of the accuracy of the linear models is inevitable before they could be used, because practical manufacturing processes are inherently nonlinear. Such analysis is not straightforward for the high dimensionality of multi-station manufacturing processes and the complicated relationship between process parameters and the process output. This paper presents a nonlinearity analysis method for multi-station assembly processes through an integration of experiment design and data mining. It is found that certain design parameters (e.g., the ratio between locator deviations and the distance between locators) and the number of stations play an important role in the linearization error. Although demonstrated in the specific context of two dimensional multi-station assembly processes, the proposed method can be applied to investigate many characteristics of a broad variety of manufacturing process models.

In view of the case that tolerance and maintenance design have been studied separately not to concentrate reflecting the relationship between process cost and product quality in the manufacturing systems, an optimization model of integrated tolerance and maintenance design for two-dimensional multi-station assembly processes is proposed. At first, with the relationship of dimension deviation propagation analyzed, a quality loss model for multi-station assembly processes is built; Then, a statistics feature model of deviation about fixture locating pin pairs is established, with the loss of the fixture process wear considered. At last the lifecycle cost of fixture system including fabrication and maintenance is defined, a two-dimensional multi-station assembly process optimization method was proposed, tolerance and maintenance parameters of the locating pin pairs in two-dimensional multi-station assembly process are optimized and designed. Automotive body side aperture assembly is given as an example to illuminate that the method is reasonable and effective.

Product dimensional quality is one of the most important topics in machining processes. However, limited research has been done on the dimensional control and root cause identification methodologies in multi-station machining processes. This paper will study the stream of variation (SOV) problem by developing a new type of model — State Space Model — to describe the dimensional deviation of the multistation machining processes. A process level diagnostic methodology is also developed. A case study is presented to illustrate the developed modeling and diagnostics methodology.

Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

Sensor layout optimization by integrating Bayesian approach to diagnose multi-station assembly processes

Based on the assembly total cost including the fixture system life-cycle cost,the part hole fabrication cost and the product quality loss cost,the optimization method of integrated tolerancing and maintenance planning for two-dimensional multi-station assembly processes is developed.The relationship of dimensional variation propagation for two-dimensional multi-station assembly processes is analyzed,and the quality loss model of multi-station assembly processes is built.According to the fixture locating principle of 4-2-1,the deviation statistics feature model of the pin-hole locating pair is presented.Then the optimization model of integrated tolerancing and maintenance planning is proposed,in which the assembly total cost is minimized including the fixture system life-cycle cost,the part hole fabrication cost and the product quality loss cost.Automotive body side aperture assembly is given as an example to study the influences of the locating pin tolerance,the part hole tolerance,the pin replacement cycle,the fit clearance,the mean wear-out rate and wear-out variance on the assembly total cost.Furthermore,the locating pin tolerance,the part hole tolerance and the replacement cycle are optimized and designed.Compared with other separated designs such as uniform tolerance of locating pin,uniform tolerance of part hole,uniform tolerance of locating pin and part hole,and fixed replacement cycle separately,the assembly total cost of the integrated design method descreaes by 16.25%,11.31%,39.93% and 13.54% respectively.It provides a new way of designing high-quality and low-cost fixture system for product assembly.

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm.

In assembly process control and design optimization, it is critical to establish a mathematical model that describes the relationship between the dimensional quality of the final product and the various process parameters (e.g., the fixture layout and locator position deviation). This article presents a surrogate modeling methodology for multistage assembly processes to characterize the relationship between fixture layout and product dimensional quality. The mathematical structure of the model is derived from a physical analysis based on first principles and then the parameters of the model are identified using data from computer experiments. The resulting surrogate model can enable fixture layout optimization in process planning. A comprehensive case study of a multistage assembly process is also presented to demonstrate the effectiveness and high fidelity of the developed method.

The problem of fault diagnosis of steam turbine used three methods of generating units, the results show that not only the method in this paper is multiple fault diagnosis method, and the formula is more simple, more practical application results.

Multiple fault diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. The multiple fault problems is important, since the single fault assumption can lead to incorrect or failed diagnoses when multiple faults occur. In this work, we present an approach for diagnosing multi faults based on model using techniques of detection and localization. We use an observer to generate residuals for a decision in a stage of monitoring and diagnostic system when disruptions or defects occur. Our contribution is the proposal of a diagnostic method when multiple faults type actuators or sensors affect the system.

Graph-based systems are models wherein the nodes represent the components and the edges represent the fault propagation between the components. For critical systems, some components are equipped with smart sensors for on-board system health management. When an abnormal situation occurs, alarms will be triggered from these sensors. This paper considers the problem of identifying the set of potential failure sources from the set of ringing alarms in graph-based systems. However, the computational complexity of solving the optimal multiple fault diagnosis (MFD) problem is exponential. Based on Lagrangian relaxation and subgradient optimization, we present a heuristic algorithm to find approximately the most likely candidate fault set. A computationally cheaper heuristic algorithm - primal heuristic - has also been applied to the problem so that real-time MFD in systems with several thousand failure sources becomes feasible in a fraction of a second. This paper also considers systems with asymmetric and multivalued alarms (tests).

In this paper, a new approach to alarm processing in power systems is proposed based on the set covering theory and a refined genetic algorithm (RGA). Although alarm processing and fault diagnosis have different explanations in power system community, from the viewpoint of artificial intelligence community, the alarm processing problem is a typical Multiple Fault Diagnosis (MFD) problem. Thus, we first attribute the alarm processing problem to a MFD problem, and discuss the MFD problem in general with special emphasis on the evaluation criteria. And then, a probabilistic criterion is introduced to describe the alarm processing problem, and this criterion is more reasonable man the currently used criteria. Thirdly, a new method is presented to solve the alarm processing problem using a RAG. Finally, two sample examples are used to demonstrate the feasibility and efficiency of the developed method. Test results suggest that the developed RAG-based probabilistic method to the alarm processing problem is promising.

With the development of the processing capacity of the embedded chip, it is possible to implement a machine learning algorithm in the embedded system. To achieve the fault status without poor portability, tricky threshold selection, and complex rulemaking, this article proposes a multimodal deep residual filter network (DRFN) for online multiple open-switch fault diagnosis of T-type three-level inverters. It contains low-rank matrix fusion (LMF), a DRFN, and a cross-transformer mechanism. The LMF fuses the voltage signal and the current signal for obtaining the unified representation. Then, the DRFN filters noise adaptively and extracts information effectively. Finally, the cross-transformer mechanism outputs the fault state of the T-type three-level inverter. The datasets consist of the dc-link voltage and load side current of the inverter control system. The data time window is selected as 20 ms. Through the real-time calculation of online monitored data, the experimental results show the effectiveness of the proposed fault diagnosis approach. Moreover, the accuracy of fault diagnosis is 99.18%, and the average open-circuit fault diagnosis time is 21 ms.