## 1 Introduction  
Fault diagnosis is the basis of the maintenance work for complex equipment and systems, such as the components in an aircraft [1; 2], a motor [3; 4; 5], a diesel engine [6], and so on [7]. In practical applications, multiple fault states may exist in a complex system, while various available tests can be applied for fault detection [8; 9; 10]. How to locate the potential multiple faults for further maintenance work is an important issue [11]. Because of the complexity of the system under test, it is not easy to build a mathematical model for a complex system. Thus, the method based on a signal flow model, which is mainly based on the information flow of the system, is popular among researchers for theoretical research and practical applications [12; 13]. After analyzing and building the signal flow graphs model of a complex system, the dependency model can be generated for the following fault diagnosis and maintenance work. The dependency model is a promising method to analyze the correlation between the tests and the possible failures in the equipment [14]. In general, dependency model-based fault diagnosis methods can be divided into two categories:  
* The sequential diagnosis method [15; 16; 17; 18]: Sequential diagnosis is a kind of testing and diagnosis method basing on a cycle process of \"test-diagnosis-retest\". The sequential diagnosis method is especially suitable for a system which is at the phase of principle testing and prototype developing. To obtain the correct diagnosis results with the minimum test cost, an optimal test sequence in the diagnosis strategy should be constructed, which is a typical NP (Non-deterministic Polynomial)-complete problem. Currently, more research focuses on the hypotheses of single fault and reliable test results to reduce the hardship of solving the problem. * Concurrent fault diagnosis method [19; 20; 21]: The concurrent fault diagnosis method is a traditional monitoring diagnosis method, where all the tests are completed before applying the diagnosis procedure. The whole diagnosis process only needs one cycle of testing and diagnosis procedure. Since it requires all the symptoms or at least most of the symptoms in advance to gain the correct diagnosis results, the concurrent fault diagnosis method is more suitable for a system where abundant sensing information can be acquired. A typical method for concurrent diagnosis is that the fault prior probability of the components in the system is known, the posterior probability of each component can be calculated and the maximum posteriori probability will be used to determine the corresponding fault component. The limitation of this method is that it only searches for one optimal solution and the suboptimal solution will be abandoned, which may easily fall into the local optimum. To address the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new simple dependency model-based fault diagnosis method is proposed in this paper. The two-valued logic, where both the fault and the test have only two Boolean logic values of 0 or 1, is adopted to address the problem, which means the dependency relation is a certain correlation. The proposed method is different from the fuzzy logic-based method [22; 23; 24], where the fuzzy logic mainly focuses on an uncertain or fuzzy correlation, which is represented by the membership function. The two-valued logic can describe a certain correlation between a fault and a test. The knowledge included in the test results is applied to formulate the fault ambiguity group, including multiple fault types in the system, while the prior probability of each fault type will be used to calculate the ranking of the potential fault types in the fault ambiguity group. An illustrative experiment and a case study are applied to verify the effectiveness of the proposed method. This rest of this paper is organized as follows. The problem formulation is introduced in Section 2. In Section 3, a new multiple fault diagnosis method is proposed.