## 1 Introduction  
With the rapid development of computation and communication techniques, information science is increasingly becoming the research focus [1; 2; 3]. As an application of information science, robotics and intelligent systems are frequently utilized in industrial fields that require high safety, reliability and accuracy. Examples can be found in the advanced automated production and inspection line, mining, disposal of hazardous materials. However, robotics systems are always plagued by failures for many reasons such as incorrect operation and mechanical failure. Accurate fault detection and health assessment of robotics systems are significant, especially for robot manipulators [4; 5; 6].  
For the past two decades, intensively investigations have been involved in fault detection of robot manipulators with different system models, see [7; 8; 9; 10; 11; 12; 13] and the references therein. So far, the fault detection for robot manipulators is still a key issue due to its great application potentials in both commercial and military areas [14; 15; 16; 17].  
Among the existing works related to fault detection for robot manipulators, the parameter estimation and the state observer are also two commonly used techniques. In [18], a model-based diagnostic scheme for actuator and sensor faults that may occur on a robot manipulator was present. The fault detection was achieved by a generalized observer scheme based on second-order sliding-mode approaches. However, the model-based detection scheme may suffer from modeling uncertainties due to various factors, such as aging equipments and internal system noise. To address it, [PERSON] and [PERSON] formulated a model-based actuator and sensor fault detection and isolation scheme for robot manipulators. They presented a nonlinear function to estimate the fault parameters with a pre-specified estimation error bound [19]. The parameter-variance model of a fault-tolerantmulti-sensor switching strategy for robot manipulators was considered in some recent studies [20; 21; 22]. A novel detection approach for multi-joint robots was proposed and a collision observer was designed iteratively through order reduction and re-selection of observer variables to improve computation efficiency [23]. Compared with the generalized state observers, the adaptive state observer shows a better performance in dealing with the disturbance [24]. By designing some adaptive nonlinear observers, a multiple fault detection scheme for robot manipulators was proposed in [25].  
Despite the above mentioned literatures involved in fault detection with modeling uncertainties, the works addressing both internal and output measurement disturbances in robot manipulators diagnostics are rare. The various disturbances in the system make it a challenging work to accurately detect the fault manipulator. Motived by this, we consider the fault detection for robot manipulators with both internal and output measurement disturbances. A novel fault detection and health assessment approach is proposed to better reflect the possibility of robot manipulator failure. We design an adaptive observer to reconstruct the robot manipulator system, and the health degree of the manipulator is determined by comparing the real system with the original system. The feasibility of the proposed fault detection algorithm and the health assessment approach are verified by numerical simulations.  
The remainder of this paper is organized as follows. We describe the kinetic model in Section 2. In Section 3, a fault detection approach for robot manipulators in disturbance condition is presented. The health assessment approach is given in Section 4, while the simulation results are provided in Section 5 for testifying the performance of our proposed approaches. Finally, the concluding remarks are provided in Section 6.