Explainable AI for Machine Fault Diagnosis: Understanding Features' Contribution in Machine Learning Models for Industrial Condition Monitoring

Although the effectiveness of machine learning (ML) for machine diagnosis has been widely established, the interpretation of the diagnosis outcomes is still an open issue. Machine learning models behave as black boxes; therefore, the contribution given by each of the selected features to the diagnosis is not transparent to the user. This work is aimed at investigating the capabilities of the SHapley Additive exPlanation (SHAP) to identify the most important features for fault detection and classification in condition monitoring programs for rotating machinery. The authors analyse the case of medium-sized bearings of industrial interest. Namely, vibration data were collected for different health states from the test rig for industrial bearings available at the Mechanical Engineering Laboratory of Politecnico di Torino. The Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) diagnosis models are explained by means of the SHAP. Accuracies higher than 98.5% are achieved for both the models using the SHAP as a criterion for feature selection. It is found that the skewness and the shape factor of the vibration signal have the greatest impact on the models’ outcomes

The Polarized Cosmic Hand: IXPE Observations of PSR B1509-58/MSH 15-52

We describe IXPE polarization observations of the Pulsar Wind Nebula (PWN) MSH15-52, the `Cosmic Hand'. We find X-ray polarization across the PWN, with B field vectors generally aligned with filamentary X-ray structures. High significance polarization is seen in arcs surrounding the pulsar and toward the end of the `jet', with polarization degree PD>70%, thus approaching the maximum allowed synchrotron value. In contrast, the base of the jet has lower polarization, indicating a complex magnetic field at significant angle to the jet axis. We also detect significant polarization from PSR B1509-58 itself. Although only the central pulse-phase bin of the pulse has high individual significance, flanking bins provide lower significance detections and, in conjunction with the X-ray image and radio polarization, can be used to constrain rotating vector model solutions for the pulsar geometry.Comment: To appear in the Astrophysical Journa

The IXPE View of GRB 221009A

We present the IXPE observation of GRB 221009A, which includes upper limits on the linear polarization degree of both prompt and afterglow emission in the soft X-ray energy band. GRB 221009A is an exceptionally bright gamma-ray burst (GRB) that reached Earth on 2022 October 9 after traveling through the dust of the Milky Way. The Imaging X-ray Polarimetry Explorer (IXPE) pointed at GRB 221009A on October 11 to observe, for the first time, the 2–8 keV X-ray polarization of a GRB afterglow. We set an upper limit to the polarization degree of the afterglow emission of 13.8% at a 99% confidence level. This result provides constraints on the jet opening angle and the viewing angle of the GRB, or alternatively, other properties of the emission region. Additionally, IXPE captured halo-rings of dust-scattered photons that are echoes of the GRB prompt emission. The 99% confidence level upper limit to the prompt polarization degree depends on the background model assumption, and it ranges between ∼55% and ∼82%. This single IXPE pointing provides both the first assessment of X-ray polarization of a GRB afterglow and the first GRB study with polarization observations of both the prompt and afterglow phases

Discovery of X-ray polarization angle rotation in active galaxy Mrk 421

The magnetic field conditions in astrophysical relativistic jets can be probed by multiwavelength polarimetry, which has been recently extended to X-rays. For example, one can track how the magnetic field changes in the flow of the radiating particles by observing rotations of the electric vector position angle $\Psi$. Here we report the discovery of a $\Psi_{\mathrm x}$ rotation in the X-ray band in the blazar Mrk 421 at an average flux state. Across the 5 days of Imaging X-ray Polarimetry Explorer (IXPE) observations of 4-6 and 7-9 June 2022, $\Psi_{\mathrm x}$ rotated in total by $\geq360^\circ$. Over the two respective date ranges, we find constant, within uncertainties, rotation rates ($80 \pm 9$ and $91 \pm 8 ^\circ/\rm day$) and polarization degrees ($\Pi_{\mathrm x}=10\%\pm1\%$). Simulations of a random walk of the polarization vector indicate that it is unlikely that such rotation(s) are produced by a stochastic process. The X-ray emitting site does not completely overlap the radio/infrared/optical emission sites, as no similar rotation of $\Psi$ was observed in quasi-simultaneous data at longer wavelengths. We propose that the observed rotation was caused by a helical magnetic structure in the jet, illuminated in the X-rays by a localized shock propagating along this helix. The optically emitting region likely lies in a sheath surrounding an inner spine where the X-ray radiation is released

X-ray Polarization Observations of BL Lacertae

Blazars are a class of jet-dominated active galactic nuclei with a typical double-humped spectral energy distribution. It is of common consensus the Synchrotron emission to be responsible for the low frequency peak, while the origin of the high frequency hump is still debated. The analysis of X-rays and their polarization can provide a valuable tool to understand the physical mechanisms responsible for the origin of high-energy emission of blazars. We report the first observations of BL Lacertae performed with the Imaging X-ray Polarimetry Explorer ({IXPE}), from which an upper limit to the polarization degree $\Pi_X<$12.6\% was found in the 2-8 keV band. We contemporaneously measured the polarization in radio, infrared, and optical wavelengths. Our multiwavelength polarization analysis disfavors a significant contribution of proton synchrotron radiation to the X-ray emission at these epochs. Instead, it supports a leptonic origin for the X-ray emission in BL Lac.Comment: 17 pages, 5 figures, accepted for publication in ApJ

Magnetic Field Properties inside the Jet of Mrk 421: Multiwavelength Polarimetry Including the Imaging X-ray Polarimetry Explorer

We conducted a polarimetry campaign from radio to X-ray wavelengths of the high-synchrotron-peak (HSP) blazar Mrk 421, including Imaging X-ray Polarimetry Explorer (IXPE) measurements on 2022 December 6-8. We detected X-ray polarization of Mrk 421 with a degree of $\Pi_{\rm X}$=14$\pm$1$\%$ and an electric-vector position angle $\psi_{\rm X}$=107$\pm$3$^{\circ}$ in the 2-8 keV band. From the time variability analysis, we find a significant episodic variation in $\psi_{\rm X}$. During 7 months from the first IXPE pointing of Mrk 421 in 2022 May, $\psi_{\rm X}$ varied across the range of 0$^{\circ}$ to 180$^{\circ}$, while $\Pi_{\rm X}$ maintained similar values within $\sim$10-15$\%$. Furthermore, a swing in $\psi_{\rm X}$ in 2022 June was accompanied by simultaneous spectral variations. The results of the multiwavelength polarimetry show that the X-ray polarization degree was generally $\sim$2-3 times greater than that at longer wavelengths, while the polarization angle fluctuated. Additionally, based on radio, infrared, and optical polarimetry, we find that rotation of $\psi$ occurred in the opposite direction with respect to the rotation of $\psi_{\rm X}$ over longer timescales at similar epochs. The polarization behavior observed across multiple wavelengths is consistent with previous IXPE findings for HSP blazars. This result favors the energy-stratified shock model developed to explain variable emission in relativistic jets. The accompanying spectral variation during the $\psi_{\rm X}$ rotation can be explained by a fluctuation in the physical conditions, e.g., in the energy distribution of relativistic electrons. The opposite rotation direction of $\psi$ between the X-ray and longer-wavelength polarization accentuates the conclusion that the X-ray emitting region is spatially separated from that at longer wavelengths.Comment: 17 pages, 13 figures, 4 tables; Accepted for publication in A&

A Unified Class Model for Checking Security Policies in ICT Infrastructures

Automatic conformance checks of security policies is becoming a key issue in large ICT infrastructures that are more and more prone to cyber-attacks, in particular when wireless communication technologies and media are extensively adopted. This paper presents a hierarchical class-based model for the description of a system and its security requirements, that can be profitably used by a computer-aided analysis tool designed to carry out several kinds of policy verification. Our experience, gained in analyzing real systems, confirms that the structure and flexibility of the system description model are critical issues, together with the collection of real data needed to populate the model itself. The proposed solution is able to tolerate and overcome many of the practical limitations that are met when dealing with large and heterogeneous scenarios

Explainable AI for Machine Fault Diagnosis: Understanding Features&rsquo; Contribution in Machine Learning Models for Industrial Condition Monitoring

Although the effectiveness of machine learning (ML) for machine diagnosis has been widely established, the interpretation of the diagnosis outcomes is still an open issue. Machine learning models behave as black boxes; therefore, the contribution given by each of the selected features to the diagnosis is not transparent to the user. This work is aimed at investigating the capabilities of the SHapley Additive exPlanation (SHAP) to identify the most important features for fault detection and classification in condition monitoring programs for rotating machinery. The authors analyse the case of medium-sized bearings of industrial interest. Namely, vibration data were collected for different health states from the test rig for industrial bearings available at the Mechanical Engineering Laboratory of Politecnico di Torino. The Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) diagnosis models are explained by means of the SHAP. Accuracies higher than 98.5% are achieved for both the models using the SHAP as a criterion for feature selection. It is found that the skewness and the shape factor of the vibration signal have the greatest impact on the models&rsquo; outcomes