Abstract— This paper presents an innovative on-line approach for autonomous diagnostics and prognostics. It overcomes limitations of current diagnostics and prognostics technology by developing a “generic” framework that is relatively independent of the type of physical equipment under consideration. Proposed Diagnostics and Prognostics Framework (DPF) is based on unsupervised learning methods (reducing the need for human intervention). The procedures used in DPF are designed to temporally evolve the critical parameters with monitoring experience for enhanced diagnostic/prognostic accuracy (a critical ability for mass deployment of the technology on a variety of equipment/ hardware without needing extensive initial tune-up). This framework is currently under deployment in a major automotive manufacturing plant in Michigan, USA. Results from this pilot program to date are very satisfactory. I. INTRODUCTION IAGNOSTICS has traditionally been defined as the ability to detect and sometimes isolate a faulted component and/or failure condition [1]. Prognostics builds upon the diagnostic assessment and is defined here as the capability to predict the progression of this fault condition to component failure. In the last two decades, tremendous advances have been made in the area of sensing hardware, IT infrastructure, signal processing algorithms, and modeling methods; still, on-line diagnostics/prognostics are largely reserved for only the most critical system components. This technology has not yet found its place in health management of mainstream machinery and equipment [2]. The concept of autonomous diagnostics is based on unsupervised techniques. The term ‘unsupervised’ implies ability to learn by itself without human supervision. Autonomous diagnostics methods learn gradually from the system onto which they are deployed. Therefore, they can be deployed onto a variety of systems with ease. Once developed, no equipment specific fine-tuning is supposed to be required. A primary challenge in performing effective diagnostics of machinery and equipment is the need to achieve a high This work was supported in part by NSF DMI Grant 0300132 and Ford Motor Company. Pundarikaksha Baruah is a Doctoral Candidate at Wayne State University, Detroit, MI 48201 USA. (e-mail: baruah@wayne.edu). Ratna Babu Chinnam, Ph.D. is an Associate Professor of Industrial and Manufacturing Engineering, Wayne State University, Detroit, MI 48201 USA (corresponding author phone: 313-577-4846; fax: 303-578-5902; e- mail: r_chinnam@wayne.edu). Dimitar Filev, Ph.D. is with the Ford Motor Company, Dearborn, MI 48121, USA (e-mail: dfilev@ford.com). degree of accuracy in classifying the system’s health state in real-time given some sensory signals. While the extremely vast extant literature reports good success in developing highly effective diagnostic algorithms for certain classes of components and equipment (such as bearings, centrifugal pumps, and electrical motors), most of these successes are based on decades of academic and industrial research and extensive characterization and modeling of equipment behavior through mechanistic modeling (i.e., physics driven models) [3,4]. While such efforts are warranted in dealing with mission-critical systems (that might involve loss of life or incur large financial costs), we need cost-effective technologies that facilitate autonomous diagnostics and prognostics. The goal is to develop “generic” diagnostic and prognostic algorithms and technology that can be rapidly configured, calibrated, and refined using unsupervised learning algorithms to facilitate effective and efficient large scale deployment of CBM technology. Algorithmic novelties of the proposed autonomous diagnostics and prognostics framework (DPF) include: (i) Multi-Basis Clustering and (ii) Optimized Cluster Tracking. Multi-Basis clustering procedure combines principal component analysis (PCA) based dimensionality reduction with an unsupervised clustering technique. Initially, a single principal component (PC) transformation matrix (called raw basis) is constructed from the signal/feature data. A kernel density based unsupervised clustering technique is then employed to cluster the data in the space of the two most dominant PCs, to identify different equipment “modes of operation”. Data points from individual clusters or modes are then identified using sets of indices. A PC transformation matrix is then recomputed for each individual cluster or mode using the corresponding index set, leading to different mode basis for distinct operating mode/cluster. The diagnostics engine employs these bases for raising any pertinent alarms during equipment monitoring. Given that equipment behavior evolves due to such processes as wear-in, maintenance, and wear-out, it is critical that DPF effectively track this non-stationary behavior. To address this issue, DPF employs an optimal cluster tracking procedure using an optimal exponential weighting scheme. In particular, it employs the following two novel strategies to enhance the performance of the diagnostics engine: First, the on-line determination of an optimal exponential discounting factor ensures that the cluster tracking is effective in matching the (rate of) evolution of the equipment operating mode behavior. Secondly, the provision to allow differing exponential An Autonomous Diagnostics and Prognostics Framework for Condition-Based Maintenance Pundarikaksha Baruah, Ratna Babu Chinnam, and Dimitar Filev D 0-7803-9490-9/06/$20.00/©2006 IEEE 2006 International Joint Conference on Neural Networks Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-21, 2006 3428