Tag Archives: Hmms

Semi-Markov HMMs for modelling time series in milling machines

Kai Li, Chaochao Qiu, Xinzhao Zhou, Mingsong Chen, Yongcheng Lin, Xianshi Jia, Bin Li, Modeling and tagging of time sequence signals in the milling process based on an improved hidden semi-Markov model, Expert Systems with Applications, Volume 205, 2022 DOI: 10.1016/j.eswa.2022.117758.

Vibration signals are widely used in the field of tool wear, tool residual life prediction and health monitoring of mechanical equipment. However, the current data-driven research methods mostly rely on high-value and high-density labeled data to establish relevant models and algorithms. Therefore, it is of great significance to solve the problem of automatic tagging of data, realize automatic signal interception, and enhance the value density of manufacturing process data. The Hidden semi-Markov model (HSMM) can describe the real spatial statistical characteristics of random models through observable data. As HSMM does not need the real labels of the signal, it can reduce tagging work to improve the marking efficiency. In this paper, an improved HSMM was proposed to model and tag the spindle vibration signals in the milling process. First, the Mel frequency cepstral coefficients (MFCCs) were extracted as observation sequences from the collected spindle vibration signals, and the dimension of the original features was reduced by linear discriminant analysis (LDA). Subsequently, a signal automatic tagging model based on HSMM was developed, in which the state duration can be explicitly modeled. Finally, the evaluation of the proposed methodology was carried out in the laboratory and real industry machining. The experimental results confirmed the effectiveness and robustness of the proposed model.

On quickest change detection when the process is modelled with HMMs

Cheng-Der Fuh; Yajun Mei, Quickest Change Detection and Kullback-Leibler Divergence for Two-State Hidden Markov Models, in Signal Processing, IEEE Transactions on , vol.63, no.18, pp.4866-4878, Sept.15, 2015 DOI: 10.1109/TSP.2015.2447506

In this paper, the quickest change detection problem is studied in two-state hidden Markov models (HMM), where the vector parameter θ of the HMM changes from θ0 to θ1 at some unknown time, and one wants to detect the true change as quickly as possible while controlling the false alarm rate. It turns out that the generalized likelihood ratio (GLR) scheme, while theoretically straightforward, is generally computationally infeasible for the HMM. To develop efficient but computationally simple schemes for the HMM, we first discuss a subtlety in the recursive form of the generalized likelihood ratio (GLR) scheme for the HMM. Then we show that the recursive CUSUM scheme proposed in Fuh (Ann. Statist., 2003) can be regarded as a quasi-GLR scheme for pseudo post-change hypotheses with certain dependence structure between pre- and postchange observations. Next, we extend the quasi-GLR idea to propose recursive score schemes in the scenario when the postchange parameter θ1 of the HMM involves a real-valued nuisance parameter. Finally, the Kullback-Leibler (KL) divergence plays an essential role in the quickest change detection problem and many other fields, however it is rather challenging to numerically compute it in HMMs. Here we develop a non-Monte Carlo method that computes the KL divergence of two-state HMMs via the underlying invariant probability measure, which is characterized by the Fredholm integral equation. Numerical study demonstrates an unusual property of the KL divergence for HMM that implies the severe effects of misspecifying the postchange parameter for the HMM.

Abstracting and representing tasks performed under Learning from Demonstration, using bayesian non-parametric time-series analysis (good review of both LfD and HMMs for time-series)

Scott Niekum, Sarah Osentoski, George Konidaris, Sachin Chitta, Bhaskara Marthi, Andrew G. Barto (2015), Learning grounded finite-state representations from unstructured demonstrations, The International Journal of Robotics Research, vol. 34, pp. 131-157. DOI: 10.1177/0278364914554471

Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting the scope of possible robot competencies. Thus, the primary limiting factor of robot capabilities is often not the physical attributes of the robot, but the limited time and skill of expert programmers. One way to deal with the vast number of situations and environments that robots face outside the laboratory is to provide users with simple methods for programming robots that do not require the skill of an expert. For this reason, learning from demonstration (LfD) has become a popular alternative to traditional robot programming methods, aiming to provide a natural mechanism for quickly teaching robots. By simply showing a robot how to perform a task, users can easily demonstrate new tasks as needed, without any special knowledge about the robot. Unfortunately, LfD often yields little knowledge about the world, and thus lacks robust generalization capabilities, especially for complex, multi-step tasks. We present a series of algorithms that draw from recent advances in Bayesian non-parametric statistics and control theory to automatically detect and leverage repeated structure at multiple levels of abstraction in demonstration data. The discovery of repeated structure provides critical insights into task invariants, features of importance, high-level task structure, and appropriate skills for the task. This culminates in the discovery of a finite-state representation of the task, composed of grounded skills that are flexible and reusable, providing robust generalization and transfer in complex, multi-step robotic tasks. These algorithms are tested and evaluated using a PR2 mobile manipulator, showing success on several complex real-world tasks, such as furniture assembly.