Hidden Markov models for fault detection in dynamic systems

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making

Hidden Markov models for fault detection in dynamic systems

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making

CNC Machine Tool's wear diagnostic and prognostic by using dynamic bayesian networks.

International audienceThe failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a Computer Numerical Control (CNC) tool machine and the estimation of its Remaining Useful Life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper

Asymmetric HMMs for online ball-bearing health assessments

The degradation of critical components inside large industrial assets, such as ball-bearings, has a negative impact on production facilities, reducing the availability of assets due to an unexpectedly high failure rate. Machine learning- based monitoring systems can estimate the remaining useful life (RUL) of ball-bearings, reducing the downtime by early failure detection. However, traditional approaches for predictive systems require run-to-failure (RTF) data as training data, which in real scenarios can be scarce and expensive to obtain as the expected useful life could be measured in years. Therefore, to overcome the need of RTF, we propose a new methodology based on online novelty detection and asymmetrical hidden Markov models (As-HMM) to work out the health assessment. This new methodology does not require previous RTF data and can adapt to natural degradation of mechanical components over time in data-stream and online environments. As the system is designed to work online within the electrical cabinet of machines it has to be deployed using embedded electronics. Therefore, a performance analysis of As-HMM is presented to detect the strengths and critical points of the algorithm. To validate our approach, we use real life ball-bearing data-sets and compare our methodology with other methodologies where no RTF data is needed and check the advantages in RUL prediction and health monitoring. As a result, we showcase a complete end-to-end solution from the sensor to actionable insights regarding RUL estimation towards maintenance application in real industrial environments.This study was supported partially by the Spanish Ministry of Economy and Competitiveness through the PID2019-109247GB-I00 project and by the Spanish Ministry of Science and Innovation through the RTC2019-006871-7 (DSTREAMS project). Also, by the H2020 IoTwins project (Distributed Digital Twins for industrial SMEs: a big-data platform) funded by the EU under the call ICT-11-2018- 2019, Grant Agreement No. 857191.Peer ReviewedPostprint (author's final draft